{"ID":2879764,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15374","arxiv_id":"2508.15374","title":"Fairness for the People, by the People: Minority Collective Action","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.","short_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 d...","url_abs":"https://arxiv.org/abs/2508.15374","url_pdf":"https://arxiv.org/pdf/2508.15374v2","authors":"[\"Omri Ben-Dov\",\"Samira Samadi\",\"Amartya Sanyal\",\"Alexandru Ţifrea\"]","published":"2025-08-21T09:09:39Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.CY\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}