{"total":39,"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":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125,"area":null},{"id":"2201.09119","title":"A Causal Lens for Controllable Text Generation","created":"2022-01-22","cited_by_count":45,"area":null},{"id":"2303.01274","title":"Measuring axiomatic soundness of counterfactual image models","created":"2023-03-02","cited_by_count":34,"area":null},{"id":"2102.06648","title":"A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Models","created":"2021-02-12","cited_by_count":29,"area":null},{"id":"2110.09253","title":"A Sociotechnical View of Algorithmic Fairness","created":"2021-09-27","cited_by_count":24,"area":null},{"id":"2110.00603","title":"Algorithm Fairness in AI for Medicine and Healthcare","created":"2021-10-01","cited_by_count":21,"area":null},{"id":"2301.00346","title":"An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects","created":"2023-01-01","cited_by_count":19,"area":null},{"id":"2102.03054","title":"Removing biased data to improve fairness and accuracy","created":"2021-02-05","cited_by_count":17,"area":null},{"id":"2102.12013","title":"Understanding and Mitigating Accuracy Disparity in Regression","created":"2021-02-24","cited_by_count":17,"area":null},{"id":"2301.01481","title":"On Fairness of Medical Image Classification with Multiple Sensitive Attributes via Learning Orthogonal Representations","created":"2023-01-04","cited_by_count":17,"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":"2207.04895","title":"Bottlenecks CLUB: Unifying Information-Theoretic Trade-offs Among Complexity, Leakage, and Utility","created":"2022-07-11","cited_by_count":12,"area":null},{"id":"2302.08851","title":"On (assessing) the fairness of risk score models","created":"2023-02-17","cited_by_count":12,"area":null},{"id":"2106.06410","title":"What Can Knowledge Bring to Machine Learning? -- A Survey of Low-shot Learning for Structured Data","created":"2021-06-11","cited_by_count":10,"area":null},{"id":"2306.05066","title":"Causal Fairness for Outcome Control","created":"2023-06-08","cited_by_count":8,"area":null},{"id":"2208.09147","title":"Disentangled Representation with Causal Constraints for Counterfactual Fairness","created":"2022-08-19","cited_by_count":7,"area":null},{"id":"2301.05753","title":"Fairness and Sequential Decision Making: Limits, Lessons, and Opportunities","created":"2023-01-13","cited_by_count":5,"area":null},{"id":"2309.07085","title":"Mitigating Group Bias in Federated Learning for Heterogeneous Devices","created":"2023-09-13","cited_by_count":5,"area":null},{"id":"2312.10396","title":"How Far Can Fairness Constraints Help Recover From Biased Data?","created":"2023-12-16","cited_by_count":5,"area":null},{"id":"2106.00456","title":"Federated Estimation of Causal Effects from Observational Data","created":"2021-05-31","cited_by_count":4,"area":null},{"id":"2201.10683","title":"Promises and Challenges of Causality for Ethical Machine Learning","created":"2022-01-26","cited_by_count":4,"area":null},{"id":"2310.17687","title":"Consistent End-to-End Estimation for Counterfactual Fairness","created":"2023-10-26","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":"2212.02474","title":"Certifying Fairness of Probabilistic Circuits","created":"2022-12-05","cited_by_count":3,"area":null},{"id":"2310.12421","title":"Detecting and Mitigating Algorithmic Bias in Binary Classification using Causal Modeling","created":"2023-10-19","cited_by_count":3,"area":null},{"id":"2406.11868","title":"Ethical Framework for Responsible Foundational Models in Medical Imaging","created":"2024-04-14","cited_by_count":3,"area":null},{"id":"2205.01072","title":"The Equity Framework: Fairness Beyond Equalized Predictive Outcomes","created":"2022-04-18","cited_by_count":2,"area":null},{"id":"2106.05859","title":"A Meta Learning Approach to Discerning Causal Graph Structure","created":"2021-06-06","cited_by_count":1,"area":null},{"id":"2209.07044","title":"Fair Inference for Discrete Latent Variable Models","created":"2022-09-15","cited_by_count":1,"area":null},{"id":"2310.14064","title":"Counterfactual Prediction Under Selective Confounding","created":"2023-10-21","cited_by_count":1,"area":null},{"id":"2312.05429","title":"Mitigating Nonlinear Algorithmic Bias in Binary Classification","created":"2023-12-09","cited_by_count":1,"area":null},{"id":"2105.06591","title":"An Interpretable Graph-based Mapping of Trustworthy Machine Learning Research","created":"2021-05-13","cited_by_count":0,"area":null},{"id":"2105.14877","title":"Adaptive Multi-Source Causal Inference","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2109.07908","title":"Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education","created":"2021-09-13","cited_by_count":0,"area":null},{"id":"2307.02891","title":"BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables","created":"2023-07-06","cited_by_count":0,"area":null},{"id":"2501.07885","title":"Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling","created":"2025-01-14","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":"2606.06908","title":"polyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphs","created":"2026-06-05","cited_by_count":0,"area":null}],"next_cursor":null}