{"total":46,"rows":[{"id":"2112.13214","title":"NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification","created":"2021-12-25","cited_by_count":42,"area":null},{"id":"2207.03277","title":"A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers","created":"2022-07-07","cited_by_count":30,"area":null},{"id":"2209.00427","title":"Fair learning with Wasserstein barycenters for non-decomposable performance measures","created":"2022-09-01","cited_by_count":24,"area":null},{"id":"2212.06140","title":"Fairify: Fairness Verification of Neural Networks","created":"2022-12-08","cited_by_count":22,"area":null},{"id":"2112.04036","title":"DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs","created":"2021-12-07","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":"2212.04593","title":"Towards Understanding Fairness and its Composition in Ensemble Machine Learning","created":"2022-12-08","cited_by_count":17,"area":null},{"id":"2405.08209","title":"Who's in and who's out? A case study of multimodal CLIP-filtering in DataComp","created":"2024-05-13","cited_by_count":15,"area":null},{"id":"2110.07720","title":"Decomposing Convolutional Neural Networks into Reusable and Replaceable Modules","created":"2021-10-11","cited_by_count":14,"area":null},{"id":"2302.08018","title":"Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinking","created":"2023-02-16","cited_by_count":14,"area":null},{"id":"2407.04268","title":"NeuFair: Neural Network Fairness Repair with Dropout","created":"2024-07-05","cited_by_count":11,"area":null},{"id":"2306.09297","title":"Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML","created":"2023-06-15","cited_by_count":9,"area":null},{"id":"2308.02935","title":"Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systems","created":"2023-08-05","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":"2202.00751","title":"An Empirical Study of Modular Bias Mitigators and Ensembles","created":"2022-02-01","cited_by_count":6,"area":null},{"id":"2202.03734","title":"Cascaded Debiasing: Studying the Cumulative Effect of Multiple Fairness-Enhancing Interventions","created":"2022-02-08","cited_by_count":6,"area":null},{"id":"2401.03695","title":"A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models","created":"2024-01-08","cited_by_count":6,"area":null},{"id":"2107.08310","title":"FairBalance: How to Achieve Equalized Odds With Data Pre-processing","created":"2021-07-17","cited_by_count":5,"area":null},{"id":"2310.18593","title":"Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint","created":"2023-10-28","cited_by_count":5,"area":null},{"id":"2503.15815","title":"Attention Pruning: Automated Fairness Repair of Language Models via Surrogate Simulated Annealing","created":"2025-03-20","cited_by_count":5,"area":null},{"id":"2304.09779","title":"Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness","created":"2023-04-19","cited_by_count":4,"area":null},{"id":"2311.14901","title":"Code Search Debiasing:Improve Search Results beyond Overall Ranking Performance","created":"2023-11-25","cited_by_count":4,"area":null},{"id":"2401.07697","title":"Data vs. Model Machine Learning Fairness Testing: An Empirical Study","created":"2024-01-15","cited_by_count":4,"area":null},{"id":"2408.16683","title":"A Catalog of Fairness-Aware Practices in Machine Learning Engineering","created":"2024-08-29","cited_by_count":4,"area":null},{"id":"2406.03255","title":"On the Maximal Local Disparity of Fairness-Aware Classifiers","created":"2024-06-05","cited_by_count":3,"area":null},{"id":"2412.08167","title":"Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software","created":"2024-12-11","cited_by_count":3,"area":null},{"id":"2501.01665","title":"FairSense: Long-Term Fairness Analysis of ML-Enabled Systems","created":"2025-01-03","cited_by_count":3,"area":null},{"id":"2207.04581","title":"How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategies","created":"2022-07-11","cited_by_count":2,"area":null},{"id":"2209.08321","title":"Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instances","created":"2022-09-17","cited_by_count":2,"area":null},{"id":"2309.00416","title":"Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond","created":"2023-09-01","cited_by_count":2,"area":null},{"id":"2210.05594","title":"Navigating Ensemble Configurations for Algorithmic Fairness","created":"2022-10-11","cited_by_count":1,"area":null},{"id":"2302.05906","title":"On Comparing Fair Classifiers under Data Bias","created":"2023-02-12","cited_by_count":1,"area":null},{"id":"2306.07427","title":"Towards Fair and Explainable AI using a Human-Centered AI Approach","created":"2023-06-12","cited_by_count":1,"area":null},{"id":"2307.11379","title":"Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework","created":"2023-07-21","cited_by_count":1,"area":null},{"id":"2401.14628","title":"Inferring Data Preconditions from Deep Learning Models for Trustworthy Prediction in Deployment","created":"2024-01-26","cited_by_count":1,"area":null},{"id":"2404.19100","title":"Predicting Fairness of ML Software Configurations","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2410.06423","title":"FAIREDU: A Multiple Regression-Based Method for Enhancing Fairness in Machine Learning Models for Educational Applications","created":"2024-10-08","cited_by_count":1,"area":null},{"id":"2506.14306","title":"Fair for a few: Improving Fairness in Doubly Imbalanced Datasets","created":"2025-06-17","cited_by_count":1,"area":null},{"id":"2508.03323","title":"Software Fairness Dilemma: Is Bias Mitigation a Zero-Sum Game?","created":"2025-08-05","cited_by_count":1,"area":null},{"id":"2308.01923","title":"Fairness Improvement with Multiple Protected Attributes: How Far Are We?","created":"2023-07-25","cited_by_count":0,"area":null},{"id":"2403.19165","title":"Evaluating Fair Feature Selection in Machine Learning for Healthcare","created":"2024-03-28","cited_by_count":0,"area":null},{"id":"2406.02790","title":"Building Socially-Equitable Public Models","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2407.05176","title":"Towards Socially and Environmentally Responsible AI","created":"2024-04-23","cited_by_count":0,"area":null},{"id":"2412.15920","title":"Data Preparation for Fairness-Performance Trade-Offs: A Practitioner-Friendly Alternative?","created":"2024-12-20","cited_by_count":0,"area":null},{"id":"2504.07664","title":"Data Requirement Goal Modeling for Machine Learning Systems","created":"2025-04-10","cited_by_count":0,"area":null},{"id":"2508.16377","title":"Applications and Challenges of Fairness APIs in Machine Learning Software","created":"2025-08-22","cited_by_count":0,"area":null}],"next_cursor":null}