RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation
Algorithmic recourse methods provide counterfactual explanations that inform individuals of the actions required to overturn an unfavorable model decision. Despite rapid methodological progress, principled comparison remains elusive; existing frameworks are often difficult to extend and lack both interoperability and systematic verification that integrated methods faithfully reproduce their originally reported claims. We introduce RecourseBench, a unified evaluation framework built around three commitments: modularity, reproducibility, and interactivity. The framework decomposes the pipeline into five fully decoupled layers---Data, Preprocessing, Model, Recourse Method, and Evaluation---g...
| Authors | Hashir Ahmed, Zahra Khotanlou, Chenghao Tan et al. |
|---|---|
| Primary category | cs.AI |
| Published | 15 June 2026 |
| Last updated at source | 28 August 2026 |
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