Algorithmic Fairness

Follow this topic Ask about this topic

The field asks whether automated systems treat people and groups fairly, using criteria such as demographic parity, equalized odds, individual similarity, and causal or counterfactual fairness. Researchers audit disparities in credit, hiring, healthcare, recommendation, and language or vision models, using benchmarks such as COMPAS, Adult, and MIMIC alongside subgroup and intersectional tests. Mitigation ranges from data reweighting and representation learning to constrained or robust optimization, causal interventions, and post-hoc thresholding; work also studies fairness in rankings, sequential allocation, federated learning, and LLM agents. A central challenge is that fairness criteria can conflict with one another and with utility, while estimates and interventions may be unstable under small samples, distribution shift, strategic behavior, or uncertain causal assumptions.

Latest papers

424 papers28 papers in the last four weeks, down 20% on the four weeks before. 0.2% of all new papers.

Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation
Mar 4, 2026Yun Lu, Xiaoyu Shi, Hong Xie +2Fairness in Recommender Systems Recommender Systems
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences Chongqing, China · The First Affiliated Hospital, University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong, Hong Kong
[Re] FairDICE: A Fair Tradeoff in Multi-objective Offline RL
Mar 3, 2026Peter Adema, Karim Galliamov, Aleksey Evstratovskiy +1Offline RL Multi-Objective Reinforcement Learning
Informatics Institute, University of Amsterdam
Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
Mar 3, 2026Chongjun Xia, Xiaoyu Shi, Hong Xie +3Long-Tail Recommendation Fairness in Recommender Systems
Chinese Academy of Sciences · University of Technology Sydney · University of Science and Technology of China
Skin Tone in the Wild: A Dataset and Benchmark for 10-tone MST Classification
Mar 2, 2026Vitor Matias, Márcus Vinícius Lobo Costa, Tiago Novello +1Classification Algorithmic Fairness
Instituto de Ciências Matemáticas e de Computação (ICMC), Universidade de São Paulo (USP), São Carlos, São Paulo, Brazil · Instituto de Matemática Pura e Aplica, Rio de Janeiro, Brazil
The MAMA-MIA Challenge: Advancing Generalizability and Fairness in Breast MRI Tumor Segmentation and Treatment Response Prediction
Mar 1, 2026Lidia Garrucho, Smriti Joshi, Kaisar Kushibar +43Medical Image Segmentation Medical Image Benchmarks
DUCX: Decomposing Unfairness in Tool-Using Chest X-ray Agents
Feb 28, 2026Zikang Xu, Ruinan Jin, Xiaoxiao LiAlgorithmic Fairness Algorithmic Bias
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Anhui, China · The University of British Columbia, Vancouver, BC V6Z 1Z4, Canada · Vector Institute, Toronto, ON M5G 1M1, Canada
Fair in Mind, Fair in Action? A Synchronous Benchmark for Understanding and Generation in UMLLMs
Feb 28, 2026Yiran Zhao, Lu Zhou, Xiaogang Xu +3Algorithmic Fairness Demographic Bias in Language Models
Nanjing University of Aeronautics and Astronautics · Collaborative Innovation Center of Novel Software Technology and Industrialization · The Chinese University of Hong Kong +2
When Does Multimodal Learning Help in Healthcare? A Benchmark on EHR and Chest X-Ray Fusion
Feb 27, 2026Kejing Yin, Haizhou Xu, Wenfang Yao +5Multimodal Fusion Multimodal Model Evaluation
Department of Computer Science, Hong Kong Baptist University · Division of Artificial Intelligence, Lingnan University · School of Nursing, The Hong Kong Polytechnic University
Fairness under Graph Uncertainty: Achieving Interventional Fairness with Partially Known Causal Graphs over Clusters of Variables
Feb 27, 2026Yoichi ChikaharaAlgorithmic Fairness Causal Inference
NTT Communication Science Laboratories, Kyoto, Japan
FairQuant: Fairness-Aware Mixed-Precision Quantization for Medical Image Classification
Feb 26, 2026Thomas Woergaard, Raghavendra SelvanMixed-Precision Quantization Algorithmic Fairness
Department of Computer Science, University of Copenhagen, Denmark
Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits
Feb 26, 2026Moirangthem Tiken Singh, Amit Kalita, Sapam Jitu SinghAlgorithmic Fairness Fair Resource Allocation
Department of Computer Science and Engineering, DUIET, Dibrugarh University, Assam, India · Department of Computer Science and Engineering, MIT, Manipur University, 795003, India
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
Private and Robust Contribution Evaluation in Federated Learning
Feb 25, 2026Delio Jaramillo Velez, Gergely Biczok, Alexandre Graell i Amat +2Federated Learning Privacy-Preserving ML
University of La Laguna Tenerife, Spain · HUN-REN Hungarian Research Network Budapest, Hungary · Chalmers University Gotheburg, Sweden +2
Equitable Evaluation via Elicitation
Feb 24, 2026Elbert Du, Cynthia Dwork, Lunjia Hu +3Algorithmic Fairness Human-in-the-Loop AI
Harvard University · Northeastern University · University of Maryland +1
A Counterfactual Approach for Addressing Individual User Unfairness in Collaborative Recommender System
Feb 24, 2026Nikita Baidya, Bidyut Kr. Patra, Ratnakar DashFairness in Recommender Systems Recommender Systems
Pattern Recognition Laboratory, Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, India. · Department of Computer Science and Engineering, Indian Institute of Technology (BHU) Varanasi, Uttar Pradesh, India.
Physics-based phenomenological characterization of cross-modal bias in multimodal models
Feb 24, 2026Hyeongmo Kim, Sohyun Kang, Yerin Choi +5Multimodal Large Language Models Algorithmic Fairness
Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea · Department of Physics and Astronomy, Seoul National University, Seoul, Republic of Korea · Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea +2
SOMtime the World Ain $'$ t Fair: Violating Fairness Using Self-Organizing Maps
Feb 20, 2026Joseph Bingham, Netanel Arussy, Dvir AranUnsupervised Learning Algorithmic Fairness
Faculty of Biology, Technion University · Taub Faculty of Computer Science, Technion University
Fairness-Aware Partial-label Domain Adaptation for Voice Classification of Parkinson's and ALS
Feb 20, 2026Arianna Francesconi, Zhixiang Dai, Arthur Stefano Moscheni +7Parkinson's Disease Detection from Speech Domain Adaptation
School of Computer and Communication Sciences, EPFL (École polytechnique fédérale de Lausanne), Lausanne, Switzerland · Unit of Artificial Intelligence and Computer Systems, Università Campus Bio-Medico di Roma, Rome, Italy · Eustema S.p.A., Research and Development Centre, Naples, Italy +2
Beyond Procedure: Substantive Fairness in Conformal Prediction
Feb 18, 2026Pengqi Liu, Zijun Yu, Mouloud Belbahri +3Algorithmic Fairness Conformal Prediction
McGill University, Montreal, Canada · TD Insurance, Montreal, Canada · UQAM, Montreal, Canada +1
Fairness Dynamics in Digital Economy Platforms with Biased Ratings
Feb 18, 2026J. Martin Smit, Fernando P. SantosFairness in Recommender Systems Algorithmic Fairness
University of Amsterdam, Amsterdam, Netherlands
Temporal Panel Selection in Ongoing Citizens' Assemblies
Feb 18, 2026Yusuf Hakan Kalayci, Evi MichaSocial Choice Theory Algorithmic Fairness
University of Southern California
Fairness over Equality: Correcting Social Incentives in Asymmetric Sequential Social Dilemmas
Feb 17, 2026Alper Demir, Hüseyin Aydın, Kale-ab Abebe Tessera +2Cooperative MARL Multi-Agent Reinforcement Learning
Izmir University of Economics & University of Edinburgh, Izmir, Turkey · Middle East Technical University & Utrecht University, Ankara, Turkey · University of Edinburgh, Edinburgh, United Kingdom +1
FairRARI: A Plug and Play Framework for Fairness-Aware PageRank
Feb 9, 2026Emmanouil Kariotakis, Aritra KonarAlgorithmic Fairness Group Fairness
Department of Electrical Engineering (ESAT-STADIUS), KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
Separation-Utility Pareto Frontier: An Information-Theoretic Characterization
Feb 4, 2026Shizhou XuAlgorithmic Fairness
Department of Mathematics University of California Davis Davis, CA 95616, USA
Fair and Efficient Investment in Public Transportation
Feb 3, 2026Martin Bullinger, Edith Elkind, Kassian KöckAlgorithmic Fairness Combinatorial Optimization
School of Engineering Mathematics and Technology, University of Bristol, Bristol, UK · School of Engineering, Northwestern University, Evanston, USA · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
Improved Algorithms for Nash Welfare in Linear Bandits
Jan 30, 2026Dhruv Sarkar, Nishant Pandey, Sayak Ray ChowdhuryMulti-Armed Bandits Algorithmic Fairness
Indian Institute of Technology Kharagpur · Indian Institute of Technology Kanpur
Fairness-Aware Performance Evaluation for Multi-Party Multi-Objective Optimization
Jan 30, 2026Zifan Zhao, Peilan Xu, Wenjian LuoMulti-Party Multi-Objective Optimization Cooperative Game Theory
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China · Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Intersectional Fairness via Mixed-Integer Optimization
Jan 27, 2026Jiří Němeček, Mark Kozdoba, Illia Kryvoviaz +2Algorithmic Fairness Intersectional Fairness
Czech Technical University in Prague · Technion
Audit Me If You Can: Query-Efficient Active Fairness Auditing of Black-Box LLMs
Jan 6, 2026David Hartmann, Lena Pohlmann, Lelia Hanslik +3LLM Auditing Algorithmic Fairness
Weizenbaum Institut Berlin · Technische Universität Berlin · FIZ Karlsruhe +2
Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease
Dec 19, 2025Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz +1Group Fairness Algorithmic Fairness
Department of Health Policy, Stanford University, 615 Crothers Way Encina Commons MC6019 Stanford, CA 94305, USA · Psyfy Inc. · Department of Epidemiology and Population Health, Stanford University +1

Mar 4, 2026J/K move · Enter open · S save

Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation

Yun Lu, Xiaoyu Shi, Hong Xie, Xiangyu Zhao+1

Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences Chongqing School, University of Chinese Academy of Sciences Chongqing, China · The First Affiliated Hospital, University of Science and Technology of China Hefei, China · City University of Hong Kong Hong Kong, Hong Kong

Fairness in Recommender Systems Recommender Systems Algorithmic Fairness User Preference Modeling

Open paper AskPDF arXiv

Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping issue, but a state estimation failure. In this work, we propose DSRM-HRL, a framework that reformulates fairness-aware recommendation as a latent state purification problem followed by decoupled hierarchical decision-making. We introduce a Denoising State Representation Module (DSRM) based on diffusion models to recover the low-entropy latent preference manifold from high-entropy, noisy interaction histories. Built upon this purified state, a Hierarchical Reinforcement Learning (HRL) agent is employed to decouple conflicting objectives: a high-level policy regulates long-term fairness trajectories, while a low-level policy optimizes short-term engagement under these dynamic constraints. Extensive experiments on high-fidelity simulators (KuaiRec, KuaiRand) demonstrate that DSRM-HRL effectively breaks the "rich-get-richer" feedback loop, achieving a superior Pareto frontier between recommendation utility and exposure equity.