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  • Advances in Neural Information Processing Systems (NeurIPS)

Towards Robust and Reliable Algorithmic Recourse

By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
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Abstract

As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan approvals), there has been growing interest in post-hoc techniques which provide recourse to affected individuals. These techniques generate recourses under the assumption that the underlying predictive model does not change. However, in practice, models are often regularly updated for a variety of reasons (e.g., dataset shifts), thereby rendering previously prescribed recourses ineffective. To address this problem, we propose a novel framework, RObust Algorithmic Recourse (ROAR), that leverages adversarial training for finding recourses that are robust to model shifts. To the best of our knowledge, this work proposes the first ever solution to this critical problem. We also carry out detailed theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) we derive a lower bound on the probability of invalidation of recourses generated by existing approaches which are not robust to model shifts. 2) we prove that the additional cost incurred due to the robust recourses output by our framework is bounded. Experimental evaluation on multiple synthetic and real-world datasets demonstrates the efficacy of the proposed framework and supports our theoretical findings.

Keywords

Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction

Citation

Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic 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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    Evaluating Explainability for Graph Neural Networks

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

    By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Evaluating Explainability for Graph Neural Networks By: Chirag Agarwal, Owen Queen, Himabindu Lakkaraju and Marinka Zitnik
  • Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
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