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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Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning
Jul 18, 2026Rakshit NaiduPrivacy Auditing Group Fairness
Georgia Institute of Technology
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Jul 16, 2026Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4Graph Neural Networks Algorithmic Fairness
School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou 510007, China · Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China · School of Software Engineering, Sun Yat-sen University, Zhuhai 519082, China
Neutralizing Structural Inequality in the Nigerian FinTech Sector
Jul 11, 2026Muhammad Abdullahi SaidAlgorithmic Fairness Financial Fraud Detection
FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness
Jul 9, 2026Nick Souligne, Isabella Mixton-Garcia, Vignesh SubbianAlgorithmic Fairness Group Fairness
College of Engineering, University of Arizona, Tucson, AZ
Where to Intervene? Benchmarking Fairness-Aware Learning on Differentially Private Synthetic Tabular Data
Jul 8, 2026Vinícius Gabriel Angelozzi, Héber H. ArcoleziAlgorithmic Fairness Group Fairness
Centre Inria de l’Université Grenoble Alpes · ÉTS Montréal, Inria Grenoble
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
Jul 7, 2026Sangwon Jung, Sumin Yu, Sanghyuk Chun +1Algorithmic Fairness Counterfactual Fairness
Department of Electrical and Computer Engineering, Seoul National University · NAVER AI Lab · ASRI/INMC/IPAI/AIIS, Seoul National University
Directly Optimizing Mean Demographic Parity for Nonlinear Regression
Jul 6, 2026Ieva Petrulionyte, Julien Mairal, Michael ArbelAlgorithmic Fairness Group Fairness
Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, 38000 Grenoble, France
Who Gets Missed in the Tail? Thresholded Subgroup Underdiagnosis in Long-Tailed Chest X-ray Classification
Jul 4, 2026Ha-Hieu Pham, Hai-Dang Nguyen, Dang P. M. Cao +5Long-Tailed Classification Chest X-Ray Classification
University of Science, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam · VinUni-Illinois Smart Health Center, VinUniversity, Hanoi, Vietnam +3
Sequential Fairness Auditing with Limited Output Access
Jun 29, 2026Ioannis Pitsiorlas, Martha V. Sourla, Marios KountourisAlgorithmic Fairness Group Fairness
EURECOM, France · DaSCI, University of Granada, Spain
Fair Online Resource Allocation
Jun 17, 2026Christopher En, Yuri Faenza, Andrea Lodi +1Sequential Decision Making Fair Resource Allocation
Columbia University, IEOR Department · Cornell Tech · Universidad de Chile
No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems
Jun 16, 2026Khoat ThanAlgorithmic Fairness Group Fairness
Hanoi University of Science and Technology
A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for ranking
Jun 16, 2026Guilherme Dean Pelegrina, Renata PelissariMulti-Criteria Decision Analysis Group Fairness
Engineering School, Mackenzie Presbyterian University, 930 Consolação St, São Paulo, 01302-907, São Paulo, Brazil
α-Fair Insurance Pricing: A Fairness Continuum
Jun 12, 2026Tianhe Zhang, Xiguang Liu, Peng ShiAlgorithmic Fairness Group Fairness
Department of Risk and Insurance, Wisconsin School of Business, University of Wisconsin–Madison · Department of Information Systems and Operations Management, Warrington College of Business, University of Florida
Disparate Impact in Synthetic Data Generation
Jun 11, 2026Paul Andrey, Michaël Perrot, Batiste Le Bars +1Synthetic Data Generation Algorithmic Fairness
Univ. Lille, Inria, CNRS, Centrale Lille, UMR 9189 - CRIStal, F-59000 Lille, France
Towards Provably Fair Machine Learning: Bayesian Approaches For Consistent and Transparent Predictions
Jun 10, 2026Owen O'Neill, Fintan CostelloSelective Prediction Bayesian Inference
University College Dublin, Ireland
Learning Fair Demand Models
Jun 5, 2026Adam N. Elmachtoub, Hyemi Kim, Jonathan Y. TanGroup Fairness Dynamic Pricing
Department of Industrial Engineering and Operations Research and Data Science Institute, Columbia University, New York, NY 10027 · Department of Industrial Engineering and Operations Research, Columbia University, New York, NY 10027
UniFair: A unified fair clustering approach based on separation and compactness
Jun 3, 2026Antonia Karra, Vasiliki Papanikou, Georgios Vardakas +2Clustering Algorithmic Fairness
University of Ioannina, Greece · Archimedes/Athena Research Center, Greece
Geometric Fairness-Aware Routing for Federated Edge Networks
May 31, 2026Ratun RahmanEdge Computing Federated Graph Learning
Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?
May 31, 2026Jung-Hoon Cho, Cathy WuIntelligent Transportation Systems Group Fairness
Department of Civil and Environmental Engineering and Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA. · Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA.
Partial Fairness Awareness: Belief-Guided Strategic Mechanism for Strategic Agents
May 30, 2026Xinpeng Lv, Chunyuan Zheng, Yunxin Mao +8Strategic Classification Mechanism Design
National University of Defense Technology, Changsha, China · Peking University, Beijing, China · Shanghai University of Finance and Economics, Shanghai, China +2
COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
May 30, 2026Sheng'en Li, Dongmian ZouAlgorithmic Fairness Counterfactual Fairness
Zu Chongzhi Center and DIRC, Duke Kunshan University, Kunshan, China.
Demystifying the Optimal Fair Classifier in Multi-Class Classification
May 30, 2026Li Zhang, Yuyuan Li, XiaoHua Feng +3Algorithmic Fairness Group Fairness
College of Computer Science and Technology, Zhejiang University · School of Communication Engineering, Hangzhou Dianzi University
Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging
May 28, 2026Milad Masroor, Cuong Nguyen, Kevin Wells +1Medical Image Analysis Algorithmic Fairness
Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK
Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settings
May 27, 2026M. Generali Lince, V. Divol, R. Flamary +2Algorithmic Fairness Group Fairness
Inria, Soda team · CREST, ENSAE, IP Paris · Inria, Fairplay joint team +1
Examining Agents' Bias Amplification versus Suppression in Multi-Agent Systems
May 27, 2026Zejian Eric Wu, Zhongyi Jiang, Yuan Zhuang +1Multi-Agent LLM Systems Algorithmic Fairness
Oregon State University · Independent Researcher · Amazon +1
Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales
May 27, 2026Myeongsoo Kim, Eunji Kim, Minwoo Chae +1Diffusion Model Guidance Classifier-Free Guidance
1POSTECH · 2Amazon
Test-Time Collective Action: Proxy-Based Perturbations for Correcting Algorithmic Harms
May 26, 2026Meghana Bhange, Ulrich Aïvodji, Elliot CreagerAlgorithmic Fairness Group Fairness
ÉTS Montréal, Mila · University of Waterloo, Vector Institute
A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes
May 24, 2026John Arthur Junior, Abdul Lateef Yussif, Maame G. Asante-Mensah +3Algorithmic Fairness Algorithmic Bias
Department of Computer Science and Information Technology, University of Cape Coast, Cape Coast, Ghana.
Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification
May 19, 2026Nikhil Cherian Kurian, Victor Caquilpan Parra, Abin Shoby +6Algorithmic Fairness Medical Image Classification
Australian Institute for Machine Learning, Adelaide University, Adelaide, Australia · GlaxoSmithKline (GSK)
Auditing Discriminatory Patterns in Mortgage Lending Through Association Rules and Fair Binning
May 16, 2026Archit Rathod, Dhwani Chande, Het NagdaFinancial Services Algorithmic Auditing
University of Illinois Chicago

Jul 18, 2026, cs.LGJ/K move · Enter open · S save

Privacy Cost as Equity Input: A Group Fairness Criterion for Differentially Private Machine Learning

Rakshit Naidu

Georgia Institute of Technology

Privacy Auditing Group Fairness

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Differential privacy (DP) is increasingly deployed to limit membership inference risk in machine-learning systems. Prior work has shown that DP-SGD can widen accuracy disparities across demographic groups, but this framing treats fairness as a purely outcome-side concern. We argue that privacy cost, the information leakage borne by each group, is itself a form of harm, and adopt a compensatory-fairness framework in which a group that involuntarily bears greater privacy exposure is owed proportionally greater benefit from the system. From this principle we derive the \emph{Privacy-Cost Equity Ratio} (PCER), a group fairness metric defined as a group's positive prediction rate normalized by its per-group overfitting gap. By a standard membership inference bound, this overfitting gap upper-bounds each group's vulnerability to inference attacks, making PCER a conservative measure of benefit relative to exposure. PCER needs only per-group train and test accuracy (no shadow models), making it a practical post-hoc audit tool. We evaluate PCER alongside standard fairness metrics across six benchmark--attribute combinations spanning tabular and NLP domains, under DP-SGD at a range of privacy budgets, and validate the overfitting-gap proxy against a direct threshold membership-inference attack. The results reveal patterns that outcome-based metrics miss. On COMPAS, PCER uncovers a persistent double disadvantage: the protected group bears both greater privacy exposure and worse predictive outcomes, something demographic parity gap masks entirely. Sensitivity analysis shows very strong privacy guarantees collapse both groups' overfitting to a numerical floor, rendering exposure-based audits uninformative in that regime. Together, these findings show that fairness audits of privacy-preserving systems must account for who bears the cost of protection, not only who benefits from its outcomes.

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