Learning with Conflicts of Interest
The paper titled 'Learning with Conflicts of Interest' addresses the misalignment between the interests of machine learning (ML) system owners and users. It highlights the biases in ML systems that can lead users to make poor decisions. The authors propose a game-theoretic framework to model these conflicts and suggest algorithms to enhance beneficial interactions while minimizing bias.
- ▪Financial, social, and political factors often create conflicts of interest between ML system owners and users.
- ▪Current solutions require ML systems to implement protocols to mitigate biases, but owners often resist these changes.
- ▪The proposed framework aims to protect users from biased information while allowing them to benefit from ML systems.
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Computer Science > Machine Learning arXiv:2605.15504 (cs) [Submitted on 15 May 2026] Title:Learning with Conflicts of Interest Authors:Nischal Aryal, Arash Termehchy, Ali Vakilian, Marianne Winslett View a PDF of the paper titled Learning with Conflicts of Interest, by Nischal Aryal and 3 other authors View PDF HTML (experimental) Abstract:Financial, social, and political factors often prevent the interests of the owners of ML systems and services and their users from being perfectly aligned. ML systems often produce biased information that can influence users to make decisions that are not in their best interest. Current solution approaches require ML systems to implement protocols to mitigate their biases.
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