arXiv:2602.04155v2 Announce Type: replace-cross Abstract: 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 Kal
The increasing deployment of AI into diverse societal applications necessitates robust frameworks for ensuring fairness and equitable outcomes, particularly across different demographic groups.
This research introduces a novel game-theoretic approach to AI fairness, moving beyond simplistic robust optimization to consider bargaining solutions, which could lead to more nuanced and practical fairness standards.
The understanding and implementation of 'fairness' in AI systems may evolve from purely minimizing worst-case scenarios to considering relative improvements and the negotiation of equitable risk reduction among subpopulations.
- · AI ethicists and researchers
- · Developers of AI fairness tools
- · Populations underserved by current AI models
- · AI systems deploying unexamined 'fairness' approaches
- · Developers relying solely on simple robust optimization methods
AI developers will need to integrate more complex fairness algorithms and evaluate models using bargaining-based metrics.
Regulatory bodies might adopt standards for AI fairness that incorporate principles of relative improvement and bargaining, requiring more sophisticated compliance methods.
Increased trust in AI systems due to perceived fairness could accelerate adoption in sensitive sectors, contingent on the practical efficacy of these new methods.
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Read at arXiv cs.LG