FairPO: Robust Preference Optimization for Fair Multi-Label Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908678040322048 |
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| author | Mondal, Soumen Kumar Chanda, Prateek Varmora, Akshit Ramakrishnan, Ganesh |
| author_facet | Mondal, Soumen Kumar Chanda, Prateek Varmora, Akshit Ramakrishnan, Ganesh |
| contents | Multi-label classification (MLC) often suffers from performance disparities across labels. We propose \textbf{FairPO}, a framework combining preference-based loss and group-robust optimization to improve fairness by targeting underperforming labels. FairPO partitions labels into a \textit{privileged} set for targeted improvement and a \textit{non-privileged} set to maintain baseline performance. For privileged labels, a DPO-inspired preference loss addresses hard examples by correcting ranking errors between true labels and their confusing counterparts. A constrained objective maintains performance for non-privileged labels, while a Group Robust Preference Optimization (GRPO) formulation adaptively balances both objectives to mitigate bias. We also demonstrate FairPO's versatility with reference-free variants using Contrastive (CPO) and Simple (SimPO) Preference Optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02433 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FairPO: Robust Preference Optimization for Fair Multi-Label Learning Mondal, Soumen Kumar Chanda, Prateek Varmora, Akshit Ramakrishnan, Ganesh Machine Learning Artificial Intelligence Multi-label classification (MLC) often suffers from performance disparities across labels. We propose \textbf{FairPO}, a framework combining preference-based loss and group-robust optimization to improve fairness by targeting underperforming labels. FairPO partitions labels into a \textit{privileged} set for targeted improvement and a \textit{non-privileged} set to maintain baseline performance. For privileged labels, a DPO-inspired preference loss addresses hard examples by correcting ranking errors between true labels and their confusing counterparts. A constrained objective maintains performance for non-privileged labels, while a Group Robust Preference Optimization (GRPO) formulation adaptively balances both objectives to mitigate bias. We also demonstrate FairPO's versatility with reference-free variants using Contrastive (CPO) and Simple (SimPO) Preference Optimization. |
| title | FairPO: Robust Preference Optimization for Fair Multi-Label Learning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.02433 |