{"total":18,"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":"2105.12195","title":"Bias in Machine Learning Software: Why? How? What to do?","created":"2021-05-25","cited_by_count":93,"area":null},{"id":"2106.06054","title":"Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline","created":"2021-06-02","cited_by_count":49,"area":null},{"id":"2202.06196","title":"Fairness-aware Configuration of Machine Learning Libraries","created":"2022-02-13","cited_by_count":28,"area":null},{"id":"2110.01109","title":"FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes","created":"2021-10-03","cited_by_count":19,"area":null},{"id":"2209.07190","title":"Adaptive Fairness Improvement Based on Causality Analysis","created":"2022-09-15","cited_by_count":17,"area":null},{"id":"2303.08485","title":"Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML","created":"2023-03-15","cited_by_count":16,"area":null},{"id":"2110.13029","title":"Fair Enough: Searching for Sufficient Measures of Fairness","created":"2021-10-25","cited_by_count":14,"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":"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":"2111.02038","title":"Fair-SSL: Building fair ML Software with less data","created":"2021-11-03","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":"2212.04183","title":"Mind the Gap: Measuring Generalization Performance Across Multiple Objectives","created":"2022-12-08","cited_by_count":3,"area":null},{"id":"2404.19100","title":"Predicting Fairness of ML Software Configurations","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2504.12587","title":"Software Engineering Principles for Fairer Systems: Experiments with GroupCART","created":"2025-04-17","cited_by_count":1,"area":null},{"id":"2408.06102","title":"Contexts Matter: An Empirical Study on Contextual Influence in Fairness Testing for Deep Learning Systems","created":"2024-08-12","cited_by_count":0,"area":null},{"id":"2504.17066","title":"Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching","created":"2025-04-23","cited_by_count":0,"area":null},{"id":"2608.26209","title":"Fairness Invariants: A Relational Approach to Explaining and Mitigating Fairness Bugs","created":"2026-08-26","cited_by_count":0,"area":null}],"next_cursor":null}