FairPO: Robust Preference Optimization for Fair Multi-Label Learning

TL;DR

FairPO introduces a preference-based, group-robust framework for fair multi-label classification, effectively improving underperforming labels while maintaining overall performance and reducing bias.

Contribution

The paper presents FairPO, a novel framework combining preference loss and group-robust optimization to enhance fairness in multi-label learning.

Findings

Improves fairness by targeting underperforming labels.

Balances performance across label groups effectively.

Demonstrates versatility with reference-free variants.

Abstract

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.