{"total":70,"rows":[{"id":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125,"area":null},{"id":"2106.14574","title":"Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics","created":"2021-06-28","cited_by_count":83,"area":null},{"id":"2108.08435","title":"Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning","created":"2021-08-19","cited_by_count":50,"area":null},{"id":"2105.02317","title":"A Step Toward More Inclusive People Annotations for Fairness","created":"2021-05-05","cited_by_count":46,"area":null},{"id":"2308.01118","title":"A Survey on Popularity Bias in Recommender Systems","created":"2023-08-02","cited_by_count":43,"area":null},{"id":"2211.05568","title":"Unbiased Supervised Contrastive Learning","created":"2022-11-10","cited_by_count":40,"area":null},{"id":"2108.07403","title":"FARF: A Fair and Adaptive Random Forests Classifier","created":"2021-08-17","cited_by_count":37,"area":null},{"id":"2203.11852","title":"Representation Bias in Data: A Survey on Identification and Resolution Techniques","created":"2022-03-22","cited_by_count":37,"area":null},{"id":"2106.02705","title":"Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning","created":"2021-06-04","cited_by_count":34,"area":null},{"id":"2205.13619","title":"Fairness in Recommendation: Foundations, Methods and Applications","created":"2022-05-26","cited_by_count":27,"area":null},{"id":"2101.09385","title":"Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems","created":"2021-01-23","cited_by_count":26,"area":null},{"id":"2102.12586","title":"A Stochastic Optimization Framework for Fair Risk Minimization","created":"2021-02-24","cited_by_count":26,"area":null},{"id":"2203.16024","title":"Longitudinal Fairness with Censorship","created":"2022-03-30","cited_by_count":22,"area":null},{"id":"2103.06172","title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","created":"2021-03-10","cited_by_count":21,"area":null},{"id":"2110.02932","title":"Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development","created":"2021-10-06","cited_by_count":21,"area":null},{"id":"2102.06764","title":"Technical Challenges for Training Fair Neural Networks","created":"2021-02-12","cited_by_count":20,"area":null},{"id":"2205.05396","title":"A Survey on Fairness for Machine Learning on Graphs","created":"2022-05-11","cited_by_count":20,"area":null},{"id":"2212.08645","title":"Efficient Conditionally Invariant Representation Learning","created":"2022-12-16","cited_by_count":19,"area":null},{"id":"2111.03015","title":"Modeling Techniques for Machine Learning Fairness: A Survey","created":"2021-11-04","cited_by_count":17,"area":null},{"id":"2312.02592","title":"FRAPPE: A Group Fairness Framework for Post-Processing Everything","created":"2023-12-05","cited_by_count":16,"area":null},{"id":"2301.12364","title":"Fair Decision-making Under Uncertainty","created":"2023-01-29","cited_by_count":15,"area":null},{"id":"2105.06604","title":"Towards Equity and Algorithmic Fairness in Student Grade Prediction","created":"2021-05-14","cited_by_count":13,"area":null},{"id":"2206.02792","title":"FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Data","created":"2022-06-06","cited_by_count":13,"area":null},{"id":"2202.10848","title":"Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals","created":"2022-02-22","cited_by_count":11,"area":null},{"id":"2109.12422","title":"Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models","created":"2021-09-25","cited_by_count":10,"area":null},{"id":"2207.03444","title":"Fairness and Bias in Robot Learning","created":"2022-07-07","cited_by_count":10,"area":null},{"id":"2410.10995","title":"Watching the Watchers: Exposing Gender Disparities in Machine Translation Quality Estimation","created":"2024-10-14","cited_by_count":10,"area":null},{"id":"2109.08549","title":"Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach","created":"2021-09-17","cited_by_count":9,"area":null},{"id":"2212.12603","title":"Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints","created":"2022-12-23","cited_by_count":9,"area":null},{"id":"2305.11348","title":"In the Name of Fairness: Assessing the Bias in Clinical Record De-identification","created":"2023-05-18","cited_by_count":9,"area":null},{"id":"2401.14702","title":"FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently","created":"2024-01-26","cited_by_count":8,"area":null},{"id":"2105.09985","title":"Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective","created":"2021-05-20","cited_by_count":7,"area":null},{"id":"2303.01692","title":"Travel Demand Forecasting: A Fair AI Approach","created":"2023-03-03","cited_by_count":7,"area":null},{"id":"2306.00025","title":"Disentangling and Operationalizing AI Fairness at LinkedIn","created":"2023-05-30","cited_by_count":7,"area":null},{"id":"2309.17337","title":"Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools","created":"2023-09-29","cited_by_count":7,"area":null},{"id":"2507.02087","title":"Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions","created":"2025-07-02","cited_by_count":7,"area":null},{"id":"2409.18470","title":"Fairness without Sensitive Attributes via Knowledge Sharing","created":"2024-09-27","cited_by_count":6,"area":null},{"id":"2107.01979","title":"Machine Learning for Fraud Detection in E-Commerce: A Research Agenda","created":"2021-07-05","cited_by_count":5,"area":null},{"id":"2307.14668","title":"Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment","created":"2023-07-27","cited_by_count":5,"area":null},{"id":"2311.06826","title":"Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms","created":"2023-11-12","cited_by_count":5,"area":null},{"id":"2402.18129","title":"On the Inductive Biases of Demographic Parity-based Fair Learning Algorithms","created":"2024-02-28","cited_by_count":5,"area":null},{"id":"2206.14853","title":"Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale","created":"2022-06-29","cited_by_count":4,"area":null},{"id":"2303.13790","title":"Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2310.03146","title":"Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data","created":"2023-10-04","cited_by_count":3,"area":null},{"id":"2401.00093","title":"Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System","created":"2023-12-29","cited_by_count":3,"area":null},{"id":"2402.03540","title":"Regulation Games for Trustworthy Machine Learning","created":"2024-02-05","cited_by_count":3,"area":null},{"id":"2406.16738","title":"Inducing Group Fairness in Prompt-Based Language Model Decisions","created":"2024-06-24","cited_by_count":3,"area":null},{"id":"2509.08163","title":"Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation","created":"2025-09-09","cited_by_count":3,"area":null},{"id":"2204.12941","title":"Unsupervised Learning of Unbiased Visual Representations","created":"2022-04-26","cited_by_count":2,"area":null},{"id":"2302.00025","title":"On the Within-Group Fairness of Screening Classifiers","created":"2023-01-31","cited_by_count":2,"area":null},{"id":"2307.06518","title":"Machine Learning practices and infrastructures","created":"2023-07-13","cited_by_count":2,"area":null},{"id":"2312.10508","title":"TrojFair: Trojan Fairness Attacks","created":"2023-12-16","cited_by_count":2,"area":null},{"id":"2408.13667","title":"Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factors","created":"2024-08-24","cited_by_count":2,"area":null},{"id":"2506.17035","title":"Critical Appraisal of Fairness Metrics in Clinical Predictive AI","created":"2025-06-20","cited_by_count":2,"area":null},{"id":"2307.04105","title":"Towards Assumption-free Bias Mitigation","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2307.05728","title":"Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification","created":"2023-07-11","cited_by_count":1,"area":null},{"id":"2411.10544","title":"Debias-CLR: A Contrastive Learning Based Debiasing Method for Algorithmic Fairness in Healthcare Applications","created":"2024-11-15","cited_by_count":1,"area":null},{"id":"2503.14925","title":"pFedFair: Towards Optimal Group Fairness-Accuracy Trade-off in Heterogeneous Federated Learning","created":"2025-03-19","cited_by_count":1,"area":null},{"id":"2510.01281","title":"An Analysis of the New EU AI Act and A Proposed Standardization Framework for Machine Learning Fairness","created":"2025-09-30","cited_by_count":1,"area":null},{"id":"2603.13373","title":"Ethical Fairness in Ubiquitous Health Sensing without Known Attributes","created":"2026-03-10","cited_by_count":1,"area":null},{"id":"2106.14861","title":"Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments","created":"2021-06-28","cited_by_count":0,"area":null},{"id":"2304.03745","title":"Assessing Perceived Fairness from Machine Learning Developer's Perspective","created":"2023-04-07","cited_by_count":0,"area":null},{"id":"2311.03532","title":"The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing","created":"2023-11-06","cited_by_count":0,"area":null},{"id":"2503.16233","title":"Empirical Analysis of Privacy-Fairness-Accuracy Trade-offs in Federated Learning: A Step Towards Responsible AI","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2505.19441","title":"Fairness-in-the-Workflow: How Machine Learning Practitioners at Big Tech Companies Approach Fairness in Recommender Systems","created":"2025-05-26","cited_by_count":0,"area":null},{"id":"2511.00359","title":"Toward Unifying Group Fairness Evaluation from a Sparsity Perspective","created":"2025-11-01","cited_by_count":0,"area":null},{"id":"2512.07249","title":"IFFair: Influence Function-driven Sample Reweighting for Fair Classification","created":"2025-12-08","cited_by_count":0,"area":null},{"id":"2603.21393","title":"A Generalised Exponentiated Gradient Approach to Enhance Fairness in Binary and Multi-class Classification Tasks","created":"2026-03-22","cited_by_count":0,"area":null},{"id":"2607.08493","title":"Ensemble Diversity Optimization for Subjective Supervision","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2608.10431","title":"What We Know about Responsible AI Practices in Industry: A Half Decade of Empirical Research","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}