Machine learning models that provide recourse to adversely affected individuals. These actions may fail to account for latent characteristics that are not directly used by a model (e.g., sex, age, socio-economic conditions). In this paper, we study how the cost and feasibility of recourse can change across latent groups. We introduce a notion of group-level plausibility to identify groups that may be related to one another through latent characteristics related to immutable categorical features. We develop a general-purpose clustering procedure to identify groups from samples. We develop a constrained optimization approach to build models that equalizes the recourse cost across latent groups. We evaluate this machinery through an empirical study on simulated and real-world datasets, showing that models trained using our optimization approach have better performance in terms of overall recourse costs and group feasibility.

Providing Fair Recourse over Plausible Groups

Safe, Robust and Responsible AI

technical paper

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AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

jayanth yetukuri

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Providing Fair Recourse over Plausible Groups

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