{"total":36,"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":"2403.04547","title":"CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?","created":"2024-03-07","cited_by_count":35,"area":null},{"id":"2107.06720","title":"Fairness in Ranking under Uncertainty","created":"2021-07-14","cited_by_count":27,"area":null},{"id":"2106.03783","title":"An Information-theoretic Approach to Distribution Shifts","created":"2021-06-07","cited_by_count":25,"area":null},{"id":"2202.09724","title":"Bayes-Optimal Classifiers under Group Fairness","created":"2022-02-20","cited_by_count":24,"area":null},{"id":"2403.08743","title":"Prompting Fairness: Integrating Causality to Debias Large Language Models","created":"2024-03-13","cited_by_count":21,"area":null},{"id":"2106.12887","title":"A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models","created":"2021-06-06","cited_by_count":19,"area":null},{"id":"2304.14252","title":"FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations","created":"2023-04-27","cited_by_count":18,"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":"2205.07182","title":"Fair Bayes-Optimal Classifiers Under Predictive Parity","created":"2022-05-15","cited_by_count":16,"area":null},{"id":"2205.15860","title":"A Reduction to Binary Approach for Debiasing Multiclass Datasets","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2205.15935","title":"Bias-inducing geometries: an exactly solvable data model with fairness implications","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2206.03150","title":"Group Meritocratic Fairness in Linear Contextual Bandits","created":"2022-06-07","cited_by_count":9,"area":null},{"id":"2106.08812","title":"Costs and Benefits of Fair Regression","created":"2021-06-16","cited_by_count":8,"area":null},{"id":"2412.04642","title":"Improving LLM Group Fairness on Tabular Data via In-Context Learning","created":"2024-12-05","cited_by_count":6,"area":null},{"id":"2211.15072","title":"FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee","created":"2022-11-28","cited_by_count":5,"area":null},{"id":"2407.15453","title":"Regression under demographic parity constraints via unlabeled post-processing","created":"2024-07-22","cited_by_count":5,"area":null},{"id":"2207.07723","title":"More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias","created":"2022-07-15","cited_by_count":3,"area":null},{"id":"2407.19655","title":"AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Bias","created":"2024-07-29","cited_by_count":3,"area":null},{"id":"2503.16456","title":"Position: Beyond Assistance -- Reimagining LLMs as Ethical and Adaptive Co-Creators in Mental Health Care","created":"2025-02-21","cited_by_count":3,"area":null},{"id":"2202.04504","title":"Prediction Sensitivity: Continual Audit of Counterfactual Fairness in Deployed Classifiers","created":"2022-02-09","cited_by_count":2,"area":null},{"id":"2308.09464","title":"Data augmentation and explainability for bias discovery and mitigation in deep learning","created":"2023-08-18","cited_by_count":2,"area":null},{"id":"2308.11254","title":"A survey on bias in machine learning research","created":"2023-08-22","cited_by_count":2,"area":null},{"id":"2402.17106","title":"Achievable Fairness on Your Data With Utility Guarantees","created":"2024-02-27","cited_by_count":2,"area":null},{"id":"2403.07780","title":"FairRR: Pre-Processing for Group Fairness through Randomized Response","created":"2024-03-12","cited_by_count":2,"area":null},{"id":"2106.08086","title":"Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT)","created":"2021-06-15","cited_by_count":1,"area":null},{"id":"2107.03207","title":"Bias-Tolerant Fair Classification","created":"2021-07-07","cited_by_count":1,"area":null},{"id":"2108.05523","title":"Fair Decision-Making for Food Inspections","created":"2021-08-12","cited_by_count":1,"area":null},{"id":"2201.03092","title":"Uncovering the Source of Machine Bias","created":"2022-01-09","cited_by_count":1,"area":null},{"id":"2208.00651","title":"De-biased Representation Learning for Fairness with Unreliable Labels","created":"2022-08-01","cited_by_count":1,"area":null},{"id":"2402.10567","title":"InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2511.07032","title":"Fair Bayesian Data Selection via Generalized Discrepancy Measures","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2411.08297","title":"TowerDebias: A Novel Unfairness Removal Method Based on the Tower Property","created":"2024-11-13","cited_by_count":0,"area":null},{"id":"2510.04926","title":"Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification","created":"2025-10-06","cited_by_count":0,"area":null},{"id":"2604.02017","title":"Demographic Parity Tails for Regression","created":"2026-04-02","cited_by_count":0,"area":null},{"id":"2610.15015","title":"Algorithmic Fairness Over Time: Advances & Prospects","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}