RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

arXiv:2606.16113v2 Announce Type: replace Abstract: 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---governed by abstract interfaces and a dynamic registry. Every integrated method is classified into a four-tier reproducibility taxonomy based on artifact availability, followed by a systematic verification of its core empirical claims. We further provide an interactive web interface for flexible, configuration-driven exploration across datasets, model architectures, methods, and evaluations. To our knowledge, RecourseBench is the first benchmark to explicitly ground recourse evaluation in structured claim verification and mathematically rigorous reproducibility standards, all while featuring the largest collection of state-of-the-art recourse algorithms (27 in total).

Abstract: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---governed by abstract interfaces and a dynamic registry. Every integrated method is classified into a four-tier reproducibility taxonomy based on artifact availability, followed by a systematic verification of its core empirical claims. We further provide an interactive web interface for flexible, configuration-driven exploration across datasets, model architectures, methods, and evaluations. To our knowledge, RecourseBench is the first benchmark to explicitly ground recourse evaluation in structured claim verification and mathematically rigorous reproducibility standards, all while featuring the largest collection of state-of-the-art recourse algorithms (27 in total).

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.16113 [cs.AI]

(or arXiv:2606.16113v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2606.16113 Focus to learn more arXiv-issued DOI via DataCite

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RecourseBench: A Modular Framework for Reproducible Algorithmic Recourse Evaluation

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