Intersectional Fairness

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

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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
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
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
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

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Representation Disentanglement for Fair Chest X-Ray Diagnosis

Yujie Sun, Ruizhe Li, Xiaowu Sun

Academy of Artificial Intelligence and Advanced Technology, Xi’an Jiaotong-Liverpool University, China · Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, UK

Chest X-Ray Classification Representation Disentanglement Algorithmic Bias Intersectional Fairness

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Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities across intersectional groups. We propose a single-encoder framework combining dual-level decorrelation with prototype-guided cross-group contrastive learning to reduce demographic dependence while accounting for within-class variation. We further propose Demographic Representation Alignment Reduction (DRAR), a new metric that quantifies the reduction in demographic structure within disease representations. The framework is evaluated on four classification tasks using 34,809 CheXpert test images across eight intersectional groups, defined by age, sex and ethnicity. Compared with empirical risk minimization (ERM), our method reduces the mean equalized-odds gap from 15.41% to 10.86% and the AUC gap from 5.95% to 5.01%. Our method achieves a DRAR of 59.04% relative to ERM, with only a slight decrease in mean AUC. These results demonstrate that representation disentanglement can reduce demographic bias and improve intersectional fairness. Code is available at https://github.com/06Yujie/Fair-Medical-Imaging.

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