Intersectional Fairness

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Intersectional fairness examines how model outcomes and errors differ for people at the overlap of attributes such as race, gender, age, or health status, where single-attribute averages can hide harms. Common approaches include subgroup audits with metrics such as demographic parity and equalized odds, fairness-constrained optimization, representation learning, and preprocessing or postprocessing interventions. These methods are evaluated in settings including healthcare prediction, face and speech analysis, language models, recommendation, and clustering, with attention to both predictive utility and subgroup outcomes. A central challenge is that fine-grained groups may have sparse data, while fairness metrics and mitigation effects can conflict or vary across contexts, so interventions need careful, repeated evaluation.

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26 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs
Oct 26, 2026Zheng Zhang, Yifan Wu, Yuxiang Zhou +7Language Model Bias Evaluation Intersectional Bias in Language Models
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China · Nanyang Technological University
Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation
Oct 13, 2026Agathe Fernandes Machado, Charlotte Jamotton, Arthur CharpentierGroup Fairness Intersectional Fairness
Representation Disentanglement for Fair Chest X-Ray Diagnosis
Oct 5, 2026Yujie Sun, Ruizhe Li, Xiaowu SunChest X-Ray Classification Representation Disentanglement
Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University, China · Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, UK
Beyond Demographic Balance: Multi-Metric and Intersectional Evaluation of Fairness in MIMIC-IV Mortality Prediction
Oct 1, 2026Abdullah Al Noman, Fahmid Al Rifat, Tahrima Hashem +3Algorithmic Fairness Intersectional Fairness
Virginia Tech Blacksburg, Virginia, USA · University of Texas at Arlington Arlington, Texas, USA · University of Melbourne Melbourne, Victoria, Australia +3
Initial Evaluation of Potential Bias in Coverage of Humans in Wikidata
Sep 17, 2026Clair KronkKnowledge Graphs Intersectional Fairness
Institute for Health Equity Research (IHER), Department of Population Health Science & Policy (PHSP), Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA · Department of Artificial Intelligence and Human Health (AIHH), Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
Fairness in multi-class multi-group classification problems via contextial coherent risk measures
Aug 31, 2026Darinka Dentcheva, Xiangyu TianAlgorithmic Fairness Multiclass Classification
Department of Mathematical Sciences · Stevens Institute of Technology · Hoboken, NJ 07030, USA +1
CIFA: Contextual-Intersectional Fairness Auditing for Hidden Subgroup Discovery in Face Analysis
Aug 10, 2026Nazia Aslam, Khalid Adnan Alsayed, Thomas B. Moeslund +1Algorithmic Fairness Intersectional Fairness
Aalborg University, Denmark · Pioneer Centre for AI, Denmark · Ducaltus Ltd., Newcastle upon Tyne, United Kingdom +1
COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering
Jul 20, 2026Kyungseon Lee, Hankyo Jeong, Kunwoong Kim +2Clustering Algorithmic Fairness
Department of Statistics, Seoul National University · KAIST AI
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
Data Bias Mitigation under Coverage Constraints & The Price of Fairness
Jun 18, 2026Bruno Scarone, Alfredo Viola, Renée J. MillerDebiased ML Algorithmic Fairness
Khoury College of Computer Sciences, Northeastern University, USA · Casa de Investigadores Científicos La Comarca, Uruguay · Cheriton School of Computer Science, University of Waterloo, Canada
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
Responsible Benchmarking of Fairness for Automatic Speech Recognition
May 11, 2026Felix Herron, Ange Richard, François Portet +2Algorithmic Fairness Demographic Bias in ASR
MILES Team, LAMSADE, Université Paris Dauphine-PSL (1) · GETALP Team, LIG, Université Grenoble Alpes (2) · PACTE, Université Grenoble-Alpes (3)
FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings
May 5, 2026Farjana YesminExplainable Artificial Intelligence Healthcare
Independent Researcher · Boston, MA, USA · MA
A Framework for Exploring and Disentangling Intersectional Bias: A Case Study in Fetal Ultrasound
May 1, 2026Aya Elgebaly, Joris Fournel, Benjamin Laine Jønch Jurgensen +5Algorithmic Fairness Algorithmic Bias
Technical University of Denmark, Denmark · CAMES Rigshospitalet, Denmark
MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Apr 30, 2026Jeanne Monnier, Thomas George, Frédéric Guyard +2Algorithmic Fairness Group Fairness
Orange Research, Châtillon, France · EURECOM, Sophia Antipolis, France
Why AI Harms Can't Be Fixed One Identity at a Time: What 5300 Incident Reports Reveal About Intersectionality
Apr 27, 2026Edyta Bogucka, Sanja Šćepanović, Daniele QuerciaAI Risk Management Algorithmic Bias
Nokia Bell Labs, Cambridge, United Kingdom · University of Oxford, Oxford, United Kingdom · Politecnico di Torino, Turin, Italy
Intersectional Fairness in Large Language Models
Apr 22, 2026Chaima Boufaied, Ronnie De Souza Santos, Ann BarcombAlgorithmic Fairness Language Model Bias Evaluation
University of Calgary, Calgary, Canada
Evaluating Intersectional Fairness across Clinical Machine Learning Use Cases using Fairlogue and the All of Us Research Program
Apr 7, 2026Nick Souligne, Vignesh SubbianAlgorithmic Fairness Intersectional Fairness
College of Engineering, The University of Arizona, Tucson, AZ, United States
FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Models
Apr 6, 2026Nick Souligne, Vignesh SubbianAlgorithmic Fairness Intersectional Fairness
Department of Biomedical Engineering, University of Arizona, 1127 E. James E. Rogers Way, Tucson, AZ, 85721-0020, United States
CompDiff enables fair and zero shot medical image generation across demographic intersections through compositional diffusion
Mar 17, 2026Mahmoud K. Ibrahim, Bart Elen, Chang Sun +5Healthcare Compositional Image Generation
Institute of Data Science, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · Department of Advanced Computing Sciences, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands · VITO, Belgium
MESD: A Risk-Sensitive Metric for Explanation Fairness Across Intersectional Subgroups
Mar 13, 2026Gideon Popoola, John SheppardAlgorithmic Fairness Intersectional Fairness
Gianforte School of Computing Montana State University Bozeman, MT, USA, 59715
A Closed-Form Solution for Debiasing Vision-Language Models with Utility Guarantees Across Modalities and Tasks
Mar 13, 2026Tangzheng Lian, Guanyu Hu, Yijing Ren +2Cultural Bias in VLMs Algorithmic Fairness
King’s College London · Queen Mary University of London
Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation
Mar 12, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Algorithmic Fairness
Department of Electrical Engineering & Computer Science, University of Arkansas, Fayetteville, USA
From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review
Feb 25, 2026Uttamasha Anjally Oyshi, Susan GauchFairness in Recommender Systems Recommender Systems
Department of Electrical Engineering & Computer Science University of Arkansas Fayetteville, USA
Intersectional Fairness via Mixed-Integer Optimization
Jan 27, 2026Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2Algorithmic Fairness Intersectional Fairness
Czech Technical University in Prague · Technion
How Reliable are Fairness Audits with Unreliable Data?
Jun 28, 2025Yash Vardhan TomarAlgorithmic Auditing Algorithmic Fairness
Purdue University

Oct 26, 2026J/K move · Enter open · S save

Beyond Single-Attribute Auditing: A Configurable Multi-Attribute Benchmark for Intersectional Fairness in LLMs

Zheng Zhang, Yifan Wu, Yuxiang Zhou, Qi Liu+6

State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China · Nanyang Technological University

Language Model Bias Evaluation Intersectional Bias in Language Models Intersectional Fairness Language Model Auditing

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Large language models (LLMs) are increasingly evaluated for fairness, yet most existing benchmarks audit one sensitive attribute at a time. This single-attribute paradigm can miss intersectional harms, where multiple attributes interact non-additively and produce failures that are invisible under marginal evaluation. We introduce ComFair, a configurable benchmark that converts existing fairness data into 479,066 controlled Dual- and Multi-attribute instances and pairs every augmented instance with its task-equivalent source. Across Recognition, Selection, Continuation, and Conversation, this pairing supports a unified audit of performance or bias-score shifts, how often added attributes introduce new errors or mask existing ones, and model-ranking stability. Experiments on ten LLMs show task- and subgroup-dependent sensitivity: discriminative tasks expose both transition directions even when aggregate changes are modest, generative tasks exhibit larger and directionally asymmetric shifts, and rankings vary with attribute composition. ComFair therefore provides a scalable controlled sensitivity audit---rather than a model of the natural joint distribution of identities---for testing the robustness of fairness-benchmark conclusions beyond single-attribute settings.