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

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2. Towards User Guided Actionable Recourse

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3. Towards Robust and Reliable Recourse

   Upadhyay, Sohini; Joshi, Shalmali; Lakkaraju, Himabindu (December 2021, Advances in neural information processing systems)

   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 theoretical analysis which underscores the importance of constructing recourses that are robust to model shifts: 1) We quantify the probability of invalidation for recourses generated without accounting for 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. 

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4. Learning Models for Actionable Recourse

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5. The Game Of Recourse: Simulating Algorithmic Recourse over Time to Improve Its Reliability and Fairness

   https://doi.org/10.1145/3626246.3654742  

   Bell, Andrew; Fonseca, Joao; Stoyanovich, Julia (June 2024, SIGMOD/PODS '24: Companion of the 2024 International Conference on Management of Data)

   Algorithmic recourse, or providing recommendations to individuals who receive an unfavorable outcome from an algorithmic system on how they can take action and change that outcome, is an important tool for giving individuals agency against algorithmic decision systems. Unfortunately, research on algorithmic recourse faces a fundamental challenge: there are no publicly available datasets on algorithmic recourse. In this work, we begin to explore a solution to this challenge by creating an agent-based simulation called The Game of Recourse (an homage to Conway's Game of Life) to synthesize realistic algorithmic recourse data. We designed The Game of Recourse with a focus on reliability and fairness, two areas of critical importance in socio-technical systems. 

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