Group Fairness

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14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

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Minority Collective Action for User-Side Fairness
Aug 21, 2025Omri Ben-Dov, Samira Samadi, Amartya Sanyal +1Algorithmic Fairness Group Fairness
Max Planck Institute for Intelligent Systems, Tübingen AI Center, Tübingen, Germany · Department of Computer Science, University of Copenhagen · ETH Zurich
Private Rate-Constrained Optimization with Applications to Fair Learning
May 28, 2025Mohammad Yaghini, Tudor Cebere, Michael Menart +2Primal-Dual Optimization Group Fairness
University of Toronto · Vector Institute · PreMeDICaL team +3
When fairness metrics fail: A utility-based perspective on $\varepsilon$ -fairness
May 15, 2024Tolulope Fadina, Thorsten SchmidtAlgorithmic Fairness Group Fairness
Actuarial Science and Risk Management, University of Illinois Urbana Champaign, Illinois, USA · Department of Mathematical Stochastics, University of Freiburg, Ermst-Zermelo Str. 2, 79104 Freiburg, Germany
Measuring Model-Induced Discrimination via Efficient Fairness Approximation
May 15, 2024Yijun Bian, Yujie LuoAlgorithmic Fairness Group Fairness
Department of Computer Science, University of Copenhagen, 2100 Copenhagen, Denmark · Department of Mathematics, National University of Singapore, Singapore 117543, Singapore
A Distributionally Robust Optimisation Approach to Fair Credit Scoring
Feb 2, 2024Pablo Casas, Huan Yu, Christophe MuesDistribution Shift Robustness Financial Services
Southampton Business School, University of Southampton, Southampton, SO16 7QF, United Kingdom
Proportionally Representative Clustering
Apr 27, 2023Haris Aziz, Barton E. Lee, Sean Morota Chu +1Clustering Algorithmic Fairness
UNSW Sydney · ETH Zürich · Northwestern University
Equity Promotion in Online Resource Allocation
Dec 8, 2021Pan Xu, Yifan XuSequential Decision Making Fair Resource Allocation
Department of Computer Science, New Jersey Institute of Technology, Newark, USA · Key Lab of CNII, MOE, Southeast University, Nanjing, China
Assessing Predictive Models for Fairness Based on Activity-Space Patterns
Date pendingFrancesco Lettich, Mario A. Nascimento, Chiara Pugliese +1Algorithmic Fairness Group Fairness
Institute of Information Science and Technologies, National Research Council, Pisa, Italy · Department of Computing Science, University of Alberta, Edmonton, Canada · Institute of Informatics and Telematics, National Research Council, Pisa, Italy

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Minority Collective Action for User-Side Fairness

Omri Ben-Dov, Samira Samadi, Amartya Sanyal, Alexandru Ţifrea

Max Planck Institute for Intelligent Systems, Tübingen AI Center, Tübingen, Germany · Department of Computer Science, University of Copenhagen · ETH Zurich

Algorithmic Fairness Group Fairness

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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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