Game-Theoretic Framework for ML Bias Mitigation

A new arXiv paper proposes a game-theoretic framework to address conflicts of interest between machine learning systems and their users. The authors argue that financial, social, and political factors often misalign the interests of ML system owners and users, leading to biased information that influences users against their best interest. Current solutions require ML systems to implement bias mitigation protocols, but owners lack incentives and often cite freedom of expression or business concerns. The proposed framework models the interaction as a game, using the conflict of interest to protect users from adverse information while allowing safe benefits from the systems. The paper is available on arXiv under identifier 2605.15504.