Maximin Relative Improvement: Fair Learning as a Bargaining Problem
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866914304977010688 |
|---|---|
| author | Han, Jiwoo Banerjee, Moulinath Sun, Yuekai |
| author_facet | Han, Jiwoo Banerjee, Moulinath Sun, Yuekai |
| contents | When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations. This game-theoretic perspective reveals that existing robust optimization methods such as minimizing worst-group loss or regret correspond to classical bargaining solutions and embody different fairness principles. We propose relative improvement, the ratio of actual risk reduction to potential reduction from a baseline predictor, which recovers the Kalai-Smorodinsky solution. Unlike absolute-scale methods that may not be comparable when groups have different potential predictability, relative improvement provides axiomatic justification including scale invariance and individual monotonicity. We establish finite-sample convergence guarantees under mild conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_04155 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Maximin Relative Improvement: Fair Learning as a Bargaining Problem Han, Jiwoo Banerjee, Moulinath Sun, Yuekai Machine Learning Computer Science and Game Theory When deploying a single predictor across multiple subpopulations, we propose a fundamentally different approach: interpreting group fairness as a bargaining problem among subpopulations. This game-theoretic perspective reveals that existing robust optimization methods such as minimizing worst-group loss or regret correspond to classical bargaining solutions and embody different fairness principles. We propose relative improvement, the ratio of actual risk reduction to potential reduction from a baseline predictor, which recovers the Kalai-Smorodinsky solution. Unlike absolute-scale methods that may not be comparable when groups have different potential predictability, relative improvement provides axiomatic justification including scale invariance and individual monotonicity. We establish finite-sample convergence guarantees under mild conditions. |
| title | Maximin Relative Improvement: Fair Learning as a Bargaining Problem |
| topic | Machine Learning Computer Science and Game Theory |
| url | https://arxiv.org/abs/2602.04155 |