{"total":40,"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":"2207.05302","title":"Causal Conceptions of Fairness and their Consequences","created":"2022-07-12","cited_by_count":38,"area":null},{"id":"2209.10222","title":"Fairness Reprogramming","created":"2022-09-21","cited_by_count":33,"area":null},{"id":"2202.01327","title":"Adaptive Sampling Strategies to Construct Equitable Training Datasets","created":"2022-01-31","cited_by_count":18,"area":null},{"id":"2206.04101","title":"What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective","created":"2022-06-08","cited_by_count":18,"area":null},{"id":"2302.06975","title":"A Review of the Role of Causality in Developing Trustworthy AI Systems","created":"2023-02-14","cited_by_count":14,"area":null},{"id":"2101.07361","title":"Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification","created":"2021-01-18","cited_by_count":13,"area":null},{"id":"2109.00173","title":"FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes","created":"2021-09-01","cited_by_count":13,"area":null},{"id":"2403.17333","title":"The Pursuit of Fairness in Artificial Intelligence Models: A Survey","created":"2024-03-26","cited_by_count":13,"area":null},{"id":"2202.05049","title":"Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings","created":"2022-02-10","cited_by_count":12,"area":null},{"id":"2110.15310","title":"On the Fairness of Machine-Assisted Human Decisions","created":"2021-10-28","cited_by_count":11,"area":null},{"id":"2209.13678","title":"FAIR-FATE: Fair Federated Learning with Momentum","created":"2022-09-27","cited_by_count":10,"area":null},{"id":"2212.09844","title":"Robust Design and Evaluation of Predictive Algorithms under Unobserved Confounding","created":"2022-12-19","cited_by_count":8,"area":null},{"id":"2311.05436","title":"Fair Wasserstein Coresets","created":"2023-11-09","cited_by_count":8,"area":null},{"id":"2410.09600","title":"The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning","created":"2024-10-12","cited_by_count":8,"area":null},{"id":"2301.08987","title":"Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors","created":"2023-01-21","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":"2306.03625","title":"Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy Learning","created":"2023-06-06","cited_by_count":6,"area":null},{"id":"2403.14713","title":"Auditing Fairness under Unobserved Confounding","created":"2024-03-18","cited_by_count":6,"area":null},{"id":"2301.06199","title":"Doubly Robust Counterfactual Classification","created":"2023-01-15","cited_by_count":5,"area":null},{"id":"2312.05551","title":"Multi-dimensional Fair Federated Learning","created":"2023-12-09","cited_by_count":4,"area":null},{"id":"2311.14688","title":"Procedural Fairness Through Decoupling Objectionable Data Generating Components","created":"2023-11-05","cited_by_count":3,"area":null},{"id":"2105.07852","title":"Hard Choices and Hard Limits for Artificial Intelligence","created":"2021-05-04","cited_by_count":2,"area":null},{"id":"2302.08204","title":"Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting","created":"2023-02-16","cited_by_count":2,"area":null},{"id":"2403.08425","title":"Specification Overfitting in Artificial Intelligence","created":"2024-03-13","cited_by_count":2,"area":null},{"id":"2403.17852","title":"Counterfactual Fairness through Transforming Data Orthogonal to Bias","created":"2024-03-26","cited_by_count":2,"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":"2201.06467","title":"Principled Diverse Counterfactuals in Multilinear Models","created":"2022-01-17","cited_by_count":1,"area":null},{"id":"2306.04212","title":"Migrate Demographic Group For Fair GNNs","created":"2023-06-07","cited_by_count":0,"area":null},{"id":"2309.07176","title":"Mind the Gap: Optimal and Equitable Encouragement Policies","created":"2023-09-12","cited_by_count":0,"area":null},{"id":"2502.11031","title":"A Critical Review of Predominant Bias in Neural Networks","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2508.08337","title":"Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants","created":"2025-08-10","cited_by_count":0,"area":null},{"id":"2509.17956","title":"\"I think this is fair\": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessment","created":"2025-09-22","cited_by_count":0,"area":null},{"id":"2510.00163","title":"Partial Identification Approach to Counterfactual Fairness Assessment","created":"2025-09-30","cited_by_count":0,"area":null},{"id":"2512.09810","title":"Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clustering","created":"2025-12-10","cited_by_count":0,"area":null},{"id":"2601.03453","title":"Measures of classification bias derived from sample size analysis","created":"2026-01-06","cited_by_count":0,"area":null},{"id":"2602.06984","title":"Empowering Affected Individuals to Shape AI Fairness Assessments: Processes, Criteria, and Tools","created":"2026-01-27","cited_by_count":0,"area":null},{"id":"2604.07009","title":"CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging","created":"2026-04-08","cited_by_count":0,"area":null},{"id":"2605.04073","title":"Confronting Label Indeterminacy in Automated Bail Decisions","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2608.01333","title":"A Statistical Framework for Data-Driven Discovery of Differential Performance in Clinical Risk Prediction Models","created":"2026-08-02","cited_by_count":0,"area":null}],"next_cursor":null}