Fairness for the People, by the People: Minority Collective Action

• URL: http://arxiv.org/abs/2508.15374v1
• Date: Thu, 21 Aug 2025 09:09:39 GMT
• Title: Fairness for the People, by the People: Minority Collective Action
• Authors: Omri Ben-Dov, Samira Samadi, Amartya Sanyal, Alexandru Ţifrea,
• Abstract summary: Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups.<n>We propose a coordinated minority group strategically relabels its own data to enhance fairness, without altering the firm's training process.<n>Our findings show that a subgroup of the minority can substantially reduce unfairness with a small impact on the overall prediction error.
• Score: 50.29077265863936
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of existing firm-side bias mitigation techniques, they typically incur utility costs and require organizational buy-in. Recognizing that many models rely on user-contributed data, end-users can induce fairness through the framework of Algorithmic Collective Action, where a coordinated minority group strategically relabels its own data to enhance fairness, without altering the firm's training process. We propose three practical, model-agnostic methods to approximate ideal relabeling and validate them on real-world datasets. Our findings show that a subgroup of the minority can substantially reduce unfairness with a small impact on the overall prediction error.

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