Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Explanations in Everyday Software Systems: Towards a Taxonomy for Explainability Needs (2404.16644v1)

Published 25 Apr 2024 in cs.SE

Abstract: Modern software systems are becoming increasingly complex and opaque. The integration of explanations within software has shown the potential to address this opacity and can make the system more understandable to end-users. As a result, explainability has gained much traction as a non-functional requirement of complex systems. Understanding what type of system requires what types of explanations is necessary to facilitate the inclusion of explainability in early software design processes. In order to specify explainability requirements, an explainability taxonomy that applies to a variety of different software types is needed. In this paper, we present the results of an online survey with 84 participants. We asked the participants to state their questions and confusions concerning their three most recently used software systems and elicited both explicit and implicit explainability needs from their statements. These needs were coded by three researchers. In total, we identified and classified 315 explainability needs from the survey answers. Drawing from a large pool of explainability needs and our coding procedure, we present two major contributions of this work: 1) a taxonomy for explainability needs in everyday software systems and 2) an overview of how the need for explanations differs between different types of software systems.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (42)
  1. R. E. Kraut and L. A. Streeter, “Coordination in software development,” Commun. ACM, vol. 38, no. 3, p. 69–81, Mar. 1995.
  2. D. E. Perry, N. A. Staudenmayer, and L. G. Votta, “People, organizations, and process improvement,” IEEE Software, vol. 11, no. 4, pp. 36–45, 1994.
  3. M. Kuhrmann, P. Tell, J. Klünder, R. Hebig, Sherlock A. Licorish, and S. G. MacDonell, “Helena stage 2 results,” 2018.
  4. L. Chazette and K. Schneider, “Explainability as a non-functional requirement: challenges and recommendations,” Requirements Engineering, vol. 25, no. 4, pp. 493–514, Dec 2020. [Online]. Available: https://doi.org/10.1007/s00766-020-00333-1
  5. L. Chazette, J. Klünder, M. Balci, and K. Schneider, “How can we develop explainable systems? insights from a literature review and an interview study,” in Proceedings of the International Conference on Software and System Processes and International Conference on Global Software Engineering, ser. ICSSP’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 1–12. [Online]. Available: https://doi.org/10.1145/3529320.3529321
  6. M. A. Köhl, K. Baum, M. Langer, D. Oster, T. Speith, and D. Bohlender, “Explainability as a non-functional requirement,” in 2019 IEEE 27th International Requirements Engineering Conference (RE), 2019, pp. 363–368.
  7. R. Confalonieri, L. Coba, B. Wagner, and T. R. Besold, “A historical perspective of explainable artificial intelligence,” WIREs Data Mining and Knowledge Discovery, vol. 11, no. 1, p. e1391, 2021. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1391
  8. P. P. Angelov, E. A. Soares, R. Jiang, N. I. Arnold, and P. M. Atkinson, “Explainable artificial intelligence: an analytical review,” WIREs Data Mining and Knowledge Discovery, vol. 11, no. 5, p. e1424, 2021. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/abs/10.1002/widm.1424
  9. N. L. Goodwin, S. R. Nilsson, J. J. Choong, and S. A. Golden, “Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience,” Current Opinion in Neurobiology, vol. 73, p. 102544, 2022. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0959438822000381
  10. N. Burkart and M. F. Huber, “A survey on the explainability of supervised machine learning,” Journal of Artificial Intelligence Research, vol. 70, pp. 245–317, 2021.
  11. W. Brunotte, A. Specht, L. Chazette, and K. Schneider, “Privacy explanations – a means to end-user trust,” Journal of Systems and Software, vol. 195, p. 111545, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0164121222002217
  12. J. Doerr, D. Kerkow, T. Koenig, T. Olsson, and T. Suzuki, “Non-functional requirements in industry - three case studies adopting an experience-based nfr method,” in 13th IEEE International Conference on Requirements Engineering (RE’05), 2005, pp. 373–382.
  13. K. Lauenroth, E. Kamsties, and O. Hehlert, “Do words make a difference? an empirical study on the impact of taxonomies on the classification of requirements,” in 2017 IEEE 25th International Requirements Engineering Conference (RE), 2017, pp. 273–282.
  14. L. Chazette, W. Brunotte, and T. Speith, “Exploring explainability: A definition, a model, and a knowledge catalogue,” in 2021 IEEE 29th International Requirements Engineering Conference (RE), 2021, pp. 197–208.
  15. A. Das and P. Rad, “Opportunities and challenges in explainable artificial intelligence (XAI): A survey,” CoRR, vol. abs/2006.11371, 2020. [Online]. Available: https://arxiv.org/abs/2006.11371
  16. H. Deters, J. Droste, M. Fechner, and J. Klünder, “Explanations on demand - a technique for eliciting the actual need for explanations,” in 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 2023, pp. 345–351.
  17. F. Rossi, “Building trust in artificial intelligence,” Journal of international affairs, vol. 72, no. 1, pp. 127–134, 2018.
  18. D. Shin, “The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable ai,” International Journal of Human-Computer Studies, vol. 146, p. 102551, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1071581920301531
  19. L. Kästner, M. Langer, V. Lazar, A. Schomäcker, T. Speith, and S. Sterz, “On the relation of trust and explainability: Why to engineer for trustworthiness,” in 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), 2021, pp. 169–175.
  20. M. Usman, R. Britto, J. Börstler, and E. Mendes, “Taxonomies in software engineering: A systematic mapping study and a revised taxonomy development method,” Information and Software Technology, vol. 85, pp. 43–59, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0950584917300472
  21. S. Vegas, N. Juristo, and V. R. Basili, “Maturing software engineering knowledge through classifications: A case study on unit testing techniques,” IEEE Transactions on Software Engineering, vol. 35, no. 4, pp. 551–565, 2009.
  22. T. Speith, “A review of taxonomies of explainable artificial intelligence (xai) methods,” in Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, ser. FAccT ’22.   New York, NY, USA: Association for Computing Machinery, 2022, p. 2239–2250. [Online]. Available: https://doi.org/10.1145/3531146.3534639
  23. J. J. Ferreira and M. S. Monteiro, “What are people doing about xai user experience? a survey on ai explainability research and practice,” in Design, User Experience, and Usability. Design for Contemporary Interactive Environments, A. Marcus and E. Rosenzweig, Eds.   Cham: Springer International Publishing, 2020, pp. 56–73.
  24. K. Sokol and P. Flach, “Explainability fact sheets: A framework for systematic assessment of explainable approaches,” in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, ser. FAT* ’20.   New York, NY, USA: Association for Computing Machinery, 2020, p. 56–67. [Online]. Available: https://doi.org/10.1145/3351095.3372870
  25. F. Doshi-Velez and B. Kim, “Towards a rigorous science of interpretable machine learning,” 2017. [Online]. Available: https://arxiv.org/abs/1702.08608
  26. I. Nunes and D. Jannach, “A systematic review and taxonomy of explanations in decision support and recommender systems,” User Modeling and User-Adapted Interaction, vol. 27, no. 3, pp. 393–444, 2017.
  27. M. Unterbusch, M. Sadeghi, J. Fischbach, M. Obaidi, and A. Vogelsang, “Explanation needs in app reviews: Taxonomy and automated detection,” in 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW), 2023, pp. 102–111.
  28. J. Droste, H. Deters, J. Puglisi, and J. Klünder, “Designing end-user personas for explainability requirements using mixed methods research,” in 2023 IEEE 30th International Requirements Engineering Conference Workshops (REW), 2023.
  29. Anonymous, “Supplementary material for research paper “explanations in everyday software systems: Towards a taxonomy for explainability needs”,” January 2024, our supplementary material is available online at: https://figshare.com/s/4991cdf38f765e8c9906.
  30. A. Forward and T. C. Lethbridge, “A taxonomy of software types to facilitate search and evidence-based software engineering,” in Proceedings of the 2008 conference of the center for advanced studies on collaborative research: meeting of minds, 2008, pp. 179–191.
  31. J. L. Fleiss, “Measuring nominal scale agreement among many raters.” Psychological Bulletin, vol. 76, no. 5, pp. 378–382, 1971.
  32. R. L. Brennan and D. J. Prediger, “Coefficient kappa: Some uses, misuses, and alternatives,” vol. 41, no. 3, pp. 687–699, 1981.
  33. J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological Measurement, vol. 20, no. 1, pp. 37–46, 1960.
  34. J. R. Landis and G. G. Koch, “The measurement of observer agreement for categorical data,” Biometrics, vol. 33, no. 1, pp. 159–174, 1977.
  35. K. Sokol and P. Flach, “One explanation does not fit all: The promise of interactive explanations for machine learning transparency,” KI-Künstliche Intelligenz, vol. 34, no. 2, pp. 235–250, 2020.
  36. N. Tintarev and J. Masthoff, “Effective explanations of recommendations: user-centered design,” in Proceedings of the 2007 ACM conference on Recommender systems, 2007, pp. 153–156.
  37. W. Brunotte, J. Droste, and K. Schneider, “Context content consent–how to design user-centered privacy explanations,” in The 35th International Conference on Software Engineering & Knowledge Engineering, 2023.
  38. M. Langer, D. Oster, T. Speith, H. Hermanns, L. Kästner, E. Schmidt, A. Sesing, and K. Baum, “What do we want from explainable artificial intelligence (xai)?–a stakeholder perspective on xai and a conceptual model guiding interdisciplinary xai research,” Artificial Intelligence, vol. 296, p. 103473, 2021.
  39. H. Deters, J. Droste, and K. Schneider, “A means to what end? evaluating the explainability of software systems using goal-oriented heuristics,” in Proceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering, 2023, pp. 329–338.
  40. A. Adadi and M. Berrada, “Peeking inside the black-box: a survey on explainable artificial intelligence (xai),” IEEE access, vol. 6, pp. 52 138–52 160, 2018.
  41. Y. Zhang, P. Tiňo, A. Leonardis, and K. Tang, “A survey on neural network interpretability,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 5, no. 5, pp. 726–742, 2021.
  42. G. W. Harrison and E. E. Rutström, “Chapter 81 experimental evidence on the existence of hypothetical bias in value elicitation methods,” in Handbook of Experimental Economics Results, C. R. Plott and V. L. Smith, Eds.   Elsevier, 2008, vol. 1, pp. 752–767. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1574072207000819
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Jakob Droste (6 papers)
  2. Hannah Deters (6 papers)
  3. Martin Obaidi (13 papers)
  4. Kurt Schneider (29 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets