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  • 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
  • Format:Electronic
  • | Pages:27
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Abstract

As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means for recourse. While several approaches have been proposed to construct recourses for affected individuals, the recourses output by these methods either achieve low costs (i.e., ease-of-implementation) or robustness to small perturbations (i.e., noisy implementations of recourses), but not both due to the inherent trade-offs between the recourse costs and robustness. Furthermore, prior approaches do not provide end users with any agency over navigating the aforementioned trade-offs. In this work, we address the above challenges by proposing the first algorithmic framework which enables users to effectively manage the recourse cost vs. robustness trade-offs. More specifically, our framework Probabilistically ROBust rEcourse (PROBE) lets users choose the probability with which a recourse could get invalidated (recourse invalidation rate) if small changes are made to the recourse i.e., the recourse is implemented somewhat noisily. To this end, we propose a novel objective function which simultaneously minimizes the gap between the achieved (resulting) and desired recourse invalidation rates, minimizes recourse costs, and also ensures that the resulting recourse achieves a positive model prediction. We develop novel theoretical results to characterize the recourse invalidation rates corresponding to any given instance with regard to different classes of underlying models (e.g., linear models, tree based models etc.), and leverage these results to efficiently optimize the proposed objective. Experimental evaluation with multiple real world datasets demonstrates the efficacy of the proposed framework.

Keywords

AI and Machine Learning; Decision Choices and Conditions; Mathematical Methods

Citation

Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (forthcoming).
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

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    • 2023
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    When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions

    By: Himabindu Lakkaraju and Chiara Farronato
    • 2022
    • Advances in Neural Information Processing Systems (NeurIPS)

    Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

    By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
    • 2022
    • Advances in Neural Information Processing Systems (NeurIPS)

    Efficiently Training Low-Curvature Neural Networks

    By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
  • Efficiently Training Low-Curvature Neural Networks By: Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju and Francois Fleuret
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