Timezone: »
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 demonstrates the efficacy of the proposed framework and supports our theoretical findings.
Author Information
Sohini Upadhyay (Harvard University)
Shalmali Joshi (Harvard University (SEAS))
Hima Lakkaraju (Harvard)
More from the Same Authors
-
2021 : Towards the Unification and Robustness of Perturbation and Gradient Based Explanations »
· Sushant Agarwal · Shahin Jabbari · Chirag Agarwal · Sohini Upadhyay · Steven Wu · Hima Lakkaraju -
2021 : On the Connections between Counterfactual Explanations and Adversarial Examples »
· Martin Pawelczyk · Shalmali Joshi · Chirag Agarwal · Sohini Upadhyay · Hima Lakkaraju -
2021 : Towards a Rigorous Theoretical Analysis and Evaluation of GNN Explanations »
· Chirag Agarwal · Marinka Zitnik · Hima Lakkaraju -
2021 : What will it take to generate fairness-preserving explanations? »
· Jessica Dai · Sohini Upadhyay · Hima Lakkaraju -
2021 : Feature Attributions and Counterfactual Explanations Can Be Manipulated »
· Dylan Slack · Sophie Hilgard · Sameer Singh · Hima Lakkaraju -
2021 : On the Connections between Counterfactual Explanations and Adversarial Examples »
Martin Pawelczyk · Shalmali Joshi · Chirag Agarwal · Sohini Upadhyay · Hima Lakkaraju -
2021 : Reliable Post hoc Explanations: Modeling Uncertainty in Explainability »
Dylan Slack · Sophie Hilgard · Sameer Singh · Hima Lakkaraju -
2021 : Interpretable learning-to-defer for sequential decision-making »
Shalmali Joshi · Sonali Parbhoo · Finale Doshi-Velez -
2021 : Towards a Unified Framework for Fair and Stable Graph Representation Learning »
Chirag Agarwal · Hima Lakkaraju · Marinka Zitnik -
2021 : Reliable Post hoc Explanations: Modeling Uncertainty in Explainability »
Dylan Slack · Sophie Hilgard · Sameer Singh · Hima Lakkaraju -
2021 : Interpretable learning-to-defer for sequential decision-making »
Shalmali Joshi · Sonali Parbhoo · Finale Doshi-Velez -
2021 : On formalizing causal off-policy sequential decision-making »
Sonali Parbhoo · Shalmali Joshi · Finale Doshi-Velez -
2022 : "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts »
Haoran Zhang · Harvineet Singh · Shalmali Joshi -
2023 Tutorial: Responsible AI for Generative AI in Practice: Lessons Learned and Open Challenges »
Hima Lakkaraju · Krishnaram Kenthapadi · Nazneen Rajani -
2022 Workshop: New Frontiers in Adversarial Machine Learning »
Sijia Liu · Pin-Yu Chen · Dongxiao Zhu · Eric Wong · Kathrin Grosse · Hima Lakkaraju · Sanmi Koyejo -
2021 Workshop: ICML Workshop on Algorithmic Recourse »
Stratis Tsirtsis · Amir-Hossein Karimi · Ana Lucic · Manuel Gomez-Rodriguez · Isabel Valera · Hima Lakkaraju -
2021 : Invited Speakers' Panel »
Neeraja J Yadwadkar · Shalmali Joshi · Roberto Bondesan · Engineer Bainomugisha · Stephen Roberts -
2021 : Towards Robust and Reliable Model Explanations for Healthcare »
Hima Lakkaraju -
2021 : Ethics of developing ML in healthcare »
Shalmali Joshi -
2021 Poster: Towards the Unification and Robustness of Perturbation and Gradient Based Explanations »
Sushant Agarwal · Shahin Jabbari · Chirag Agarwal · Sohini Upadhyay · Steven Wu · Hima Lakkaraju -
2021 Spotlight: Towards the Unification and Robustness of Perturbation and Gradient Based Explanations »
Sushant Agarwal · Shahin Jabbari · Chirag Agarwal · Sohini Upadhyay · Steven Wu · Hima Lakkaraju -
2020 Poster: Robust and Stable Black Box Explanations »
Hima Lakkaraju · Nino Arsov · Osbert Bastani