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  • Advances in Neural Information Processing Systems (NeurIPS)

Learning Models for Actionable Recourse

By: Alexis Ross, Himabindu Lakkaraju and Osbert Bastani
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Abstract

As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant denied a loan) with recourse—i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability. We demonstrate the efficacy of our approach with extensive experiments on real data.

Keywords

Machine Learning Models; Recourse; Algorithm; Mathematical Methods

Citation

Ross, Alexis, Himabindu Lakkaraju, and Osbert Bastani. "Learning Models for Actionable Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

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More from the Authors
  • Altibbi: Revolutionizing Telehealth Using AI By: Himabindu Lakkaraju
  • Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis. By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
  • Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods. By: Chirag Agarwal, Marinka Zitnik and Himabindu Lakkaraju
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