{"total":18,"rows":[{"id":"2309.14345","title":"Bias Testing and Mitigation in LLM-based Code Generation","created":"2023-09-03","cited_by_count":53,"area":null},{"id":"2407.13710","title":"OxonFair: A Flexible Toolkit for Algorithmic Fairness","created":"2024-06-30","cited_by_count":12,"area":null},{"id":"2409.16965","title":"ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods","created":"2024-09-25","cited_by_count":8,"area":null},{"id":"2407.12710","title":"A Unifying Post-Processing Framework for Multi-Objective Learn-to-Defer Problems","created":"2024-07-17","cited_by_count":5,"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":"2407.17395","title":"The Costs of Pretending That There Are Data-Generating Probability Distributions in the Social World","created":"2024-07-24","cited_by_count":4,"area":null},{"id":"2408.15096","title":"Post-processing fairness with minimal changes","created":"2024-08-27","cited_by_count":3,"area":null},{"id":"2508.15374","title":"Minority Collective Action for User-Side Fairness","created":"2025-08-21","cited_by_count":3,"area":null},{"id":"2410.13831","title":"The Disparate Benefits of Deep Ensembles","created":"2024-10-17","cited_by_count":2,"area":null},{"id":"2507.08866","title":"Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyond","created":"2025-07-09","cited_by_count":2,"area":null},{"id":"2510.18473","title":"Benchmarking Fairness-aware Graph Neural Networks in Knowledge Graphs","created":"2025-10-21","cited_by_count":2,"area":null},{"id":"2510.22363","title":"Bias Begins with Data: The FairGround Corpus for Robust and Reproducible Research on Algorithmic Fairness","created":"2025-10-25","cited_by_count":2,"area":null},{"id":"2412.14724","title":"FROC: Building Fair ROC from a Trained Classifier","created":"2024-12-19","cited_by_count":1,"area":null},{"id":"2505.16638","title":"Reconsidering Fairness Through Unawareness From the Perspective of Model Multiplicity","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2405.18161","title":"Back to the Drawing Board for Fair Representation Learning","created":"2024-05-28","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":"2603.18115","title":"LLM-Augmented Computational Phenotyping of Long Covid","created":"2026-03-18","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}],"next_cursor":null}