Evaluation Functions of Algorithmic Recourse Incorporating Feature Selection Based on Relevance to Decision Criteria

Abstract

Algorithmic recourse provides counterfactual action plans –recourse– for users to overturn negative AI decisions. It typically assumes that minimizing an objective function, which measures the distance between a user’s current and desired state, generates acceptable recourse. However, recent studies question this assumption, highlighting the need to revisit the objective function. In this study, we propose a novel objective function that excludes the influence of features irrelevant to AI decisions. These features are identified based on their correlation with, importance in predictions of, or users’ self-reported irrelevance with decision outcomes. The proposed approach ensures such features remain unchanged in recourse. Using experimental data from a user study with a loan application scenario, we confirmed that minimizing the proposed objective function improves recourse acceptability. User self-reports were particularly effective in identifying irrelevant features. Based on these results, we discussed future directions for enhancing user-centered algorithmic recourse generation incorporating users’ prior knowledge.