Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Cost-Adaptive Recourse Recommendation by Adaptive Preference Elicitation (2402.15073v1)

Published 23 Feb 2024 in cs.LG

Abstract: Algorithmic recourse recommends a cost-efficient action to a subject to reverse an unfavorable machine learning classification decision. Most existing methods in the literature generate recourse under the assumption of complete knowledge about the cost function. In real-world practice, subjects could have distinct preferences, leading to incomplete information about the underlying cost function of the subject. This paper proposes a two-step approach integrating preference learning into the recourse generation problem. In the first step, we design a question-answering framework to refine the confidence set of the Mahalanobis matrix cost of the subject sequentially. Then, we generate recourse by utilizing two methods: gradient-based and graph-based cost-adaptive recourse that ensures validity while considering the whole confidence set of the cost matrix. The numerical evaluation demonstrates the benefits of our approach over state-of-the-art baselines in delivering cost-efficient recourse recommendations.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (45)
  1. Genattack: Practical black-box attacks with gradient-free optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 1111–1119, 2019.
  2. Adult. UCI Machine Learning Repository, 1996. DOI: https://doi.org/10.24432/C5XW20.
  3. Dimitri Bertsekas. Dynamic Programming and Optimal Control: Volume I. Athena Scientific, 2012.
  4. Learning preferences under noise and loss aversion: An optimization approach. Operations Research, 61(5):1190–1199, 2013.
  5. Using data mining to predict secondary school student performance. Proceedings of 5th FUture BUsiness TEChnology Conference, 2008.
  6. Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences, 2023.
  7. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml.
  8. Survey of machine learning algorithms for disease diagnostic. Journal of Intelligent Learning Systems and Applications, 9(01):1, 2017.
  9. Christopher G Harris. Making better job hiring decisions using ”human in the loop” techniques. In HumL@ ISWC, pages 16–26, 2018.
  10. Black-box adversarial attacks with limited queries and information. In International Conference on Machine Learning, pages 2137–2146. PMLR, 2018.
  11. Algorithmic recourse under imperfect causal knowledge: a probabilistic approach. Advances in Neural Information Processing Systems, 33:265–277, 2020.
  12. Algorithmic recourse: From counterfactual explanations to interventions. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, FAccT ’21, page 353–362, New York, NY, USA, 2021. Association for Computing Machinery.
  13. A survey of algorithmic recourse: contrastive explanations and consequential recommendations. ACM Computing Surveys, 55(5):1–29, 2022.
  14. Medical imaging using machine learning and deep learning algorithms: a review. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pages 1–5. IEEE, 2019.
  15. A review of robust operations management under model uncertainty. Production and Operations Management, 30(6):1927–1943, 2021.
  16. MOSEK ApS. MOSEK Optimizer API for Python 9.2.10, 2019.
  17. Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pages 607–617, 2020.
  18. Robust Bayesian recourse. In Uncertainty in Artificial Intelligence, pages 1498–1508. PMLR, 2022.
  19. Sherry Xue-Ying Ni and Anthony Man-Cho So. Mixed-integer semidefinite relaxation of joint admission control and beamforming: An SOC-based outer approximation approach with provable guarantees. In 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pages 1–5, 2018. 10.1109/SPAWC.2018.8446045.
  20. Learning model-agnostic counterfactual explanations for tabular data. In Proceedings of The Web Conference 2020, pages 3126–3132, 2020.
  21. CARLA: A Python library to benchmark algorithmic recourse and counterfactual explanation algorithms. Advances in Neural Information Processing Systems (Benchmark & Data Sets Track), 2021.
  22. Exploring counterfactual explanations through the lens of adversarial examples: A theoretical and empirical analysis. In International Conference on Artificial Intelligence and Statistics, pages 4574–4594. PMLR, 2022.
  23. Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134:113290, 2020.
  24. FACE: Feasible and actionable counterfactual explanations. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pages 344–350, 2020.
  25. Evaluating recommender systems from the user’s perspective: survey of the state of the art. User Modeling and User-Adapted Interaction, 22(4):317–355, 2012.
  26. Synthesizing action sequences for modifying model decisions. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5462–5469, 2020.
  27. Learning preferences of new users in recommender systems: an information theoretic approach. ACM SIGKDD Explorations Newsletter, 10(2):90–100, 2008.
  28. Beyond individualized recourse: Interpretable and interactive summaries of actionable recourses. Advances in Neural Information Processing Systems, 33:12187–12198, 2020.
  29. Learning models for actionable recourse. Advances in Neural Information Processing Systems, 34:18734–18746, 2021.
  30. Directive explanations for actionable explainability in machine learning applications. arXiv preprint arXiv:2102.02671, 2021.
  31. Counterfactual explanations can be manipulated. Advances in Neural Information Processing Systems, 34:62–75, 2021.
  32. A survey of contrastive and counterfactual explanation generation methods for explainable artificial intelligence. IEEE Access, 9:11974–12001, 2021.
  33. Fast polyhedral adaptive conjoint estimation. Marketing Science, 22(3):273–303, 2003.
  34. Polyhedral methods for adaptive choice-based conjoint analysis. Journal of Marketing Research, 41(1):116–131, 2004.
  35. A machine learning approach for predicting bank credit worthiness. In 2016 Third International Conference on Artificial Intelligence and Pattern Recognition (AIPR), pages 1–7. IEEE, 2016.
  36. Towards robust and reliable algorithmic recourse. In Advances in Neural Information Processing Systems 35, 2021.
  37. Actionable recourse in linear classification. In Proceedings of the Conference on Fairness, Accountability, and Transparency, FAT* ’19, page 10–19, 2019.
  38. Robust active preference elicitation. arXiv preprint arXiv:2003.01899, 2020.
  39. Counterfactual explanations and algorithmic recourses for machine learning: A review. arXiv preprint arXiv:2010.10596, 2020.
  40. Amortized generation of sequential algorithmic recourses for black-box models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 8512–8519, 2022.
  41. Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 2018.
  42. A comparative assessment of credit risk model based on machine learning——a case study of bank loan data. Procedia Computer Science, 174:141–149, 2020.
  43. Low-cost algorithmic recourse for users with uncertain cost functions. arXiv preprint arXiv:2111.01235, 2021.
  44. Actionable recourse guided by user preference, 2023.
  45. User preference learning for online social recommendation. IEEE Transactions on Knowledge and Data Engineering, 28(9):2522–2534, 2016.

Summary

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

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