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  • Article
  • 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

More from the Authors

    • June 2023
    • Transactions on Machine Learning Research (TMLR)

    When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making

    By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
    • 2023
    • Proceedings of the International Conference on Learning Representations (ICLR)

    Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse

    By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
    • April 2023
    • Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS)

    On the Privacy Risks of Algorithmic Recourse

    By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
More from the Authors
  • When Does Uncertainty Matter? Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making By: Sean McGrath, Parth Mehta, Alexandra Zytek, Isaac Lage and Himabindu Lakkaraju
  • Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
  • On the Privacy Risks of Algorithmic Recourse By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
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