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Does Explainable AI Have Moral Value? (2311.14687v1)

Published 5 Nov 2023 in cs.CY and cs.AI

Abstract: Explainable AI (XAI) aims to bridge the gap between complex algorithmic systems and human stakeholders. Current discourse often examines XAI in isolation as either a technological tool, user interface, or policy mechanism. This paper proposes a unifying ethical framework grounded in moral duties and the concept of reciprocity. We argue that XAI should be appreciated not merely as a right, but as part of our moral duties that helps sustain a reciprocal relationship between humans affected by AI systems. This is because, we argue, explanations help sustain constitutive symmetry and agency in AI-led decision-making processes. We then assess leading XAI communities and reveal gaps between the ideal of reciprocity and practical feasibility. Machine learning offers useful techniques but overlooks evaluation and adoption challenges. Human-computer interaction provides preliminary insights but oversimplifies organizational contexts. Policies espouse accountability but lack technical nuance. Synthesizing these views exposes barriers to implementable, ethical XAI. Still, positioning XAI as a moral duty transcends rights-based discourse to capture a more robust and complete moral picture. This paper provides an accessible, detailed analysis elucidating the moral value of explainability.

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In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Korsgaard, C. M. (1996a). Creating the kingdom of ends. Cambridge University Press. (35) Korsgaard, C. M. (1996b). The Sources of Normativity. Cambridge University Press. Korsgaard, (2018) Korsgaard, C. M. (2018). Fellow creatures: Our obligations to the other animals. Oxford University Press. Liao et al., (2020) Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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The Sources of Normativity. Cambridge University Press. Korsgaard, (2018) Korsgaard, C. M. (2018). Fellow creatures: Our obligations to the other animals. Oxford University Press. Liao et al., (2020) Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Korsgaard, C. M. (2018). Fellow creatures: Our obligations to the other animals. Oxford University Press. Liao et al., (2020) Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). 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In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). 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Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. 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IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Korsgaard, C. M. (1996b). The Sources of Normativity. Cambridge University Press. Korsgaard, (2018) Korsgaard, C. M. (2018). Fellow creatures: Our obligations to the other animals. Oxford University Press. Liao et al., (2020) Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Korsgaard, C. M. (2018). Fellow creatures: Our obligations to the other animals. Oxford University Press. Liao et al., (2020) Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). 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In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. 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Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Gruen, D. M., and Miller, S. (2020). Questioning the AI: informing design practices for explainable AI user experiences. In Bernhaupt, R., Mueller, F. F., Verweij, D., Andres, J., McGrenere, J., Cockburn, A., Avellino, I., Goguey, A., Bjøn, P., Zhao, S., Samson, B. P., and Kocielnik, R., editors, CHI ’20: CHI Conference on Human Factors in Computing Systems, Honolulu, HI, USA, April 25-30, 2020, pages 1–15. ACM. Liao et al., (2022) Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Liao, Q. V., Zhang, Y., Luss, R., Doshi-Velez, F., and Dhurandhar, A. (2022). Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable AI. In Hsu, J. and Yin, M., editors, Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2022, virtual, November 6-10, 2022, pages 147–159. AAAI Press. Lipton, (2018) Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. 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In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. 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Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. 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Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. 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Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lipton, Z. C. (2018). The mythos of model interpretability. Commun. ACM, 61(10):36–43. Lundberg et al., (2020) Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). 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Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., and Lee, S.-I. (2020). From local explanations to global understanding with explainable ai for trees. Nature Machine Intelligence, 2(1):2522–5839. Lundberg and Lee, (2017) Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc. Malgieri and Comandé, (2017) Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Malgieri, G. and Comandé, G. (2017). Why a Right to Legibility of Automated Decision-Making Exists in the General Data Protection Regulation. International Data Privacy Law, 7(4):243–265. May, (2015) May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). 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In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). 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Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). 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Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. May, S. C. (2015). Directed duties. Philosophy Compass, 10(8):523–532. Moor, (2006) Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Moor, J. (2006). The nature, importance, and difficulty of machine ethics. IEEE Intelligent Systems, 21(4):18–21. Nannini et al., (2023) Nannini, L., Balayn, A., and Smith, A. L. (2023). Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nannini, L., Balayn, A., and Smith, A. L. (2023). 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Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). 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Explainability in AI policies: A critical review of communications, reports, regulations, and standards in the eu, us, and UK. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, FAccT 2023, Chicago, IL, USA, June 12-15, 2023, pages 1198–1212. ACM. Navarro et al., (2021) Navarro, C. M., Kanellos, G., and Gottron, T. (2021). Desiderata for explainable AI in statistical production systems of the european central bank. In PKDD/ECML Workshops (1), volume 1524 of Communications in Computer and Information Science, pages 575–590. Springer. Nazar et al., (2021) Nazar, M., Alam, M. M., Yafi, E., and Su’ud, M. M. (2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. 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In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). 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A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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(2021). A systematic review of human–computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access, 9:153316–153348. - USA, 2021 (NIST)(NIST) - USA (2021). Artificial intelligence: Ai fundamental research - explainability. Parliament and Council, (2021) Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. 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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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(2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2021). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). 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Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
  46. Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts. Parliament and Council, (2022) Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2022). Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. 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IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). 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A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
  47. Proposal for a regulation of the european parliament and of the council laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts - general approach. Parliament and Council, (2023) Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Parliament, E. and Council (2023). Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
  48. Amendments adopted by the european parliament on 14 june 2023 on the proposal for a regulation of the european parliament and of the council on laying down harmonised rules on artificial intelligence (artificial intelligence act) and amending certain union legislative acts (com(2021)0206 – c9-0146/2021 – 2021/0106(cod))1. Phillips et al., (2021) Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Phillips, P. J., Hahn, C., Fontana, P., Yates, A., Greene, K. K., Broniatowski, D., and Przybocki, M. A. (2021). Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
  49. Four principles of explainable artificial intelligence. Ribeiro et al., (2016) Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). " why should i trust you?" explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pages 1135–1144. Robbins, (2019) Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. 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Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). 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Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. 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(2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. 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Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. 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(2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19.
  51. Robbins, S. (2019). A misdirected principle with a catch: Explicability for AI. Minds Mach., 29(4):495–514. Rudin, (2019) Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell., 1(5):206–215. Sambasivan et al., (2021) Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). 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Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. 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(2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). 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Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. K., and Aroyo, L. (2021). "everyone wants to do the model work, not the data work": Data cascades in high-stakes AI. In Kitamura, Y., Quigley, A., Isbister, K., Igarashi, T., Bjørn, P., and Drucker, S. M., editors, CHI ’21: CHI Conference on Human Factors in Computing Systems, Virtual Event / Yokohama, Japan, May 8-13, 2021, pages 39:1–39:15. ACM. Shulner-Tal et al., (2022) Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Shulner-Tal, A., Kuflik, T., and Kliger, D. (2022). Fairness, explainability and in-between: understanding the impact of different explanation methods on non-expert users’ perceptions of fairness toward an algorithmic system. Ethics Inf. Technol., 24(1):2. Unceta et al., (2020) Unceta, I., Nin, J., and Pujol, O. (2020). Copying machine learning classifiers. IEEE Access, 8:160268–160284. Vasconcelos et al., (2023) Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., and Krishna, R. (2023). Explanations can reduce overreliance on AI systems during decision-making. Proc. ACM Hum. Comput. Interact., 7(CSCW1):1–38. (60) Wachter, S., Mittelstadt, B., and Floridi, L. (2017a). Why a Right to Explanation of Automated Decision-Making Does Not Exist in the General Data Protection Regulation. International Data Privacy Law, 7(2):76–99. (61) Wachter, S., Mittelstadt, B. D., and Russell, C. (2017b). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. CoRR, abs/1711.00399. Wang et al., (2019) Wang, D., Yang, Q., Abdul, A. M., and Lim, B. Y. (2019). Designing theory-driven user-centric explainable AI. In Brewster, S. A., Fitzpatrick, G., Cox, A. L., and Kostakos, V., editors, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI 2019, Glasgow, Scotland, UK, May 04-09, 2019, page 601. ACM. Wang and Yin, (2021) Wang, X. and Yin, M. (2021). Are explanations helpful? A comparative study of the effects of explanations in ai-assisted decision-making. In Hammond, T., Verbert, K., Parra, D., Knijnenburg, B. P., O’Donovan, J., and Teale, P., editors, IUI ’21: 26th International Conference on Intelligent User Interfaces, College Station, TX, USA, April 13-17, 2021, pages 318–328. ACM. Watson, (2021) Watson, L. (2021). The right to know: Epistemic rights and why we need them. Routledge. Weil, (1952) Weil, S. (1952). The Need for Roots: Prelude to a Declaration of Duties Towards Mankind. Routledge, New York. Zerilli, (2022) Zerilli, J. (2022). Explaining machine learning decisions. Philosophy of Science, 89(1):1–19. Unceta, I., Nin, J., and Pujol, O. (2020). 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Authors (2)
  1. Joshua L. M. Brand (1 paper)
  2. Luca Nannini (4 papers)