Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917087895617536 |
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| author | Barrainkua, Ainhize De Toni, Giovanni Lozano, Jose Antonio Quadrianto, Novi |
| author_facet | Barrainkua, Ainhize De Toni, Giovanni Lozano, Jose Antonio Quadrianto, Novi |
| contents | Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have expressed concerns about the fairness guarantees within the recourse process itself. In this work, we provide a holistic theoretical characterization of unfairness in algorithmic recourse, formally linking fairness guarantees in recourse and classification, and highlighting limitations of the standard equal cost paradigm. We then introduce a novel fairness framework based on social burden, along with a practical algorithm (MISOB), broadly applicable under real-world conditions. Empirical results on real-world datasets show that MISOB reduces the social burden across all groups without compromising overall classifier accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04128 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden Barrainkua, Ainhize De Toni, Giovanni Lozano, Jose Antonio Quadrianto, Novi Machine Learning Computers and Society Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have expressed concerns about the fairness guarantees within the recourse process itself. In this work, we provide a holistic theoretical characterization of unfairness in algorithmic recourse, formally linking fairness guarantees in recourse and classification, and highlighting limitations of the standard equal cost paradigm. We then introduce a novel fairness framework based on social burden, along with a practical algorithm (MISOB), broadly applicable under real-world conditions. Empirical results on real-world datasets show that MISOB reduces the social burden across all groups without compromising overall classifier accuracy. |
| title | Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2509.04128 |