{"total":21,"rows":[{"id":"2110.00530","title":"A survey on datasets for fairness-aware machine learning","created":"2021-10-01","cited_by_count":157,"area":null},{"id":"2103.02512","title":"Approximation Algorithms for Socially Fair Clustering","created":"2021-03-03","cited_by_count":25,"area":null},{"id":"2105.14172","title":"A Stochastic Alternating Balance $k$-Means Algorithm for Fair Clustering","created":"2021-05-29","cited_by_count":10,"area":null},{"id":"2505.19139","title":"The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework","created":"2025-05-25","cited_by_count":9,"area":null},{"id":"2206.11210","title":"Constant-Factor Approximation Algorithms for Socially Fair $k$-Clustering","created":"2022-06-22","cited_by_count":8,"area":null},{"id":"2209.12756","title":"FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering","created":"2022-09-21","cited_by_count":6,"area":null},{"id":"2109.00708","title":"Efficient Algorithms For Fair Clustering with a New Fairness Notion","created":"2021-09-02","cited_by_count":5,"area":null},{"id":"2110.12020","title":"Fairness Degrading Adversarial Attacks Against Clustering Algorithms","created":"2021-10-22","cited_by_count":5,"area":null},{"id":"2102.00311","title":"Fairness through Social Welfare Optimization","created":"2021-01-30","cited_by_count":3,"area":null},{"id":"2301.03421","title":"A review of clustering models in educational data science towards fairness-aware learning","created":"2023-01-09","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":"2106.06049","title":"FiSH: Fair Spatial Hotspots","created":"2021-06-01","cited_by_count":1,"area":null},{"id":"2111.03638","title":"Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization","created":"2021-11-05","cited_by_count":1,"area":null},{"id":"2205.02052","title":"Exploring Rawlsian Fairness for K-Means Clustering","created":"2022-05-04","cited_by_count":1,"area":null},{"id":"2205.14867","title":"Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina","created":"2022-05-30","cited_by_count":1,"area":null},{"id":"2212.14467","title":"Cluster-level Group Representativity Fairness in $k$-means Clustering","created":"2022-12-29","cited_by_count":0,"area":null},{"id":"2505.06259","title":"Fair Clustering with Clusterlets","created":"2025-05-03","cited_by_count":0,"area":null},{"id":"2605.13759","title":"Fast and effective algorithms for fair clustering at scale","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2606.23515","title":"FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2607.18119","title":"COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering","created":"2026-07-20","cited_by_count":0,"area":null},{"id":"2610.19252","title":"Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}