{"total":22,"rows":[{"id":"2306.07951","title":"Questioning the Survey Responses of Large Language Models","created":"2023-06-13","cited_by_count":74,"area":null},{"id":"2309.14345","title":"Bias Testing and Mitigation in LLM-based Code Generation","created":"2023-09-03","cited_by_count":53,"area":null},{"id":"2306.07261","title":"Unprocessing Seven Years of Algorithmic Fairness","created":"2023-06-12","cited_by_count":18,"area":null},{"id":"2312.02592","title":"FRAPPE: A Group Fairness Framework for Post-Processing Everything","created":"2023-12-05","cited_by_count":16,"area":null},{"id":"2212.12603","title":"Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints","created":"2022-12-23","cited_by_count":9,"area":null},{"id":"2501.14710","title":"Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspective","created":"2025-01-24","cited_by_count":5,"area":null},{"id":"2301.10813","title":"Improving Fairness with Ensemble Combination: Margin-Dependent Bounds","created":"2023-01-25","cited_by_count":3,"area":null},{"id":"2409.17643","title":"Efficient Fairness-Performance Pareto Front Computation","created":"2024-09-26","cited_by_count":3,"area":null},{"id":"2404.18134","title":"Learning Fairer Representations with FairVIC","created":"2024-04-28","cited_by_count":2,"area":null},{"id":"2405.09251","title":"Measuring Model-Induced Discrimination via Efficient Fairness Approximation","created":"2024-05-15","cited_by_count":2,"area":null},{"id":"2409.20536","title":"Best Practices for Responsible Machine Learning in Credit Scoring","created":"2024-09-30","cited_by_count":2,"area":null},{"id":"2503.06150","title":"Do Fairness Interventions Come at the Cost of Privacy: Evaluations for Binary Classifiers","created":"2025-03-08","cited_by_count":2,"area":null},{"id":"2308.10767","title":"GBM-based Bregman Proximal Algorithms for Constrained Learning","created":"2023-08-21","cited_by_count":1,"area":null},{"id":"2408.06099","title":"Approximating Discrimination Within Models When Faced With Several Non-Binary Sensitive Attributes","created":"2024-08-12","cited_by_count":1,"area":null},{"id":"2408.12989","title":"RIFF: Inducing Rules for Fraud Detection from Decision Trees","created":"2024-08-23","cited_by_count":1,"area":null},{"id":"2504.12458","title":"M$^2$FGB: A Min-Max Gradient Boosting Framework for Subgroup Fairness","created":"2025-04-16","cited_by_count":1,"area":null},{"id":"2402.15561","title":"Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency","created":"2024-02-23","cited_by_count":0,"area":null},{"id":"2404.01521","title":"Fair MP-BOOST: Fair and Interpretable Minipatch Boosting","created":"2024-04-01","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":"2503.07066","title":"You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2506.12556","title":"Algorithmic Fairness: Not a Purely Technical but Socio-Technical Property","created":"2025-06-14","cited_by_count":0,"area":null},{"id":"2602.05713","title":"Projected Boosting with Fairness Constraints: Quantifying the Cost of Fair Training Distributions","created":"2026-02-05","cited_by_count":0,"area":null}],"next_cursor":null}