{"total":126,"rows":[{"id":"2106.00467","title":"A Clarification of the Nuances in the Fairness Metrics Landscape","created":"2021-06-01","cited_by_count":98,"area":null},{"id":"2204.09888","title":"Fairness in Graph Mining: A Survey","created":"2022-04-21","cited_by_count":76,"area":null},{"id":"2310.12560","title":"Fast Model Debias with Machine Unlearning","created":"2023-10-19","cited_by_count":72,"area":"safety"},{"id":"2201.03662","title":"Learning Fair Node Representations with Graph Counterfactual Fairness","created":"2022-01-10","cited_by_count":58,"area":null},{"id":"2406.10729","title":"A Comprehensive Survey of Foundation Models in Medicine","created":"2024-06-15","cited_by_count":56,"area":null},{"id":"2101.01673","title":"Characterizing Intersectional Group Fairness with Worst-Case Comparisons","created":"2021-01-05","cited_by_count":42,"area":null},{"id":"2103.02354","title":"Evaluating Robustness of Counterfactual Explanations","created":"2021-03-03","cited_by_count":41,"area":null},{"id":"2106.06683","title":"Assessing Multilingual Fairness in Pre-trained Multimodal Representations","created":"2021-06-12","cited_by_count":35,"area":null},{"id":"2304.03093","title":"Inductive Graph Unlearning","created":"2023-04-06","cited_by_count":31,"area":"safety"},{"id":"2205.11127","title":"Fairness in Recommender Systems: Research Landscape and Future Directions","created":"2022-05-23","cited_by_count":30,"area":null},{"id":"2303.17555","title":"Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairness","created":"2023-03-16","cited_by_count":26,"area":null},{"id":"2309.14381","title":"Survey of Social Bias in Vision-Language Models","created":"2023-09-24","cited_by_count":26,"area":"mllm"},{"id":"2110.09253","title":"A Sociotechnical View of Algorithmic Fairness","created":"2021-09-27","cited_by_count":24,"area":null},{"id":"2212.08507","title":"Robust Explanation Constraints for Neural Networks","created":"2022-12-16","cited_by_count":24,"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":"2302.06333","title":"Improving Recommendation Fairness via Data Augmentation","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2205.09927","title":"CertiFair: A Framework for Certified Global Fairness of Neural Networks","created":"2022-05-20","cited_by_count":17,"area":null},{"id":"2304.03935","title":"Last-Layer Fairness Fine-tuning is Simple and Effective for Neural Networks","created":"2023-04-08","cited_by_count":17,"area":null},{"id":"2106.07483","title":"Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI","created":"2021-06-14","cited_by_count":16,"area":null},{"id":"2205.08704","title":"Accurate Fairness: Improving Individual Fairness without Trading Accuracy","created":"2022-05-18","cited_by_count":16,"area":null},{"id":"2304.03646","title":"Fairness through Aleatoric Uncertainty","created":"2023-04-07","cited_by_count":16,"area":null},{"id":"2310.13007","title":"A Critical Survey on Fairness Benefits of Explainable AI","created":"2023-10-15","cited_by_count":16,"area":null},{"id":"2111.04986","title":"Unified Group Fairness on Federated Learning","created":"2021-11-09","cited_by_count":15,"area":null},{"id":"2110.13029","title":"Fair Enough: Searching for Sufficient Measures of Fairness","created":"2021-10-25","cited_by_count":14,"area":null},{"id":"2205.10032","title":"Survey on Fair Reinforcement Learning: Theory and Practice","created":"2022-05-20","cited_by_count":14,"area":null},{"id":"2305.13938","title":"Algorithmic Unfairness through the Lens of EU Non-Discrimination Law: Or Why the Law is not a Decision Tree","created":"2023-05-05","cited_by_count":14,"area":null},{"id":"2203.06038","title":"The Long Arc of Fairness: Formalisations and Ethical Discourse","created":"2022-03-08","cited_by_count":13,"area":null},{"id":"2202.03814","title":"Optimal Transport of Classifiers to Fairness","created":"2022-02-08","cited_by_count":12,"area":null},{"id":"2204.10464","title":"Towards Involving End-users in Interactive Human-in-the-loop AI Fairness","created":"2022-04-22","cited_by_count":12,"area":null},{"id":"2307.05494","title":"Towards Environmentally Equitable AI via Geographical Load Balancing","created":"2023-06-20","cited_by_count":10,"area":null},{"id":"2104.00507","title":"fairmodels: A Flexible Tool For Bias Detection, Visualization, And Mitigation","created":"2021-04-01","cited_by_count":9,"area":null},{"id":"2210.02516","title":"Equalizing Credit Opportunity in Algorithms: Aligning Algorithmic Fairness Research with U.S. Fair Lending Regulation","created":"2022-10-05","cited_by_count":9,"area":null},{"id":"2406.08819","title":"AIM: Attributing, Interpreting, Mitigating Data Unfairness","created":"2024-06-13","cited_by_count":9,"area":null},{"id":"2102.02137","title":"BeFair: Addressing Fairness in the Banking Sector","created":"2021-02-03","cited_by_count":8,"area":null},{"id":"2106.13346","title":"What will it take to generate fairness-preserving explanations?","created":"2021-06-24","cited_by_count":7,"area":null},{"id":"2207.11173","title":"Verifying Fairness in Quantum Machine Learning","created":"2022-07-22","cited_by_count":7,"area":null},{"id":"2310.05598","title":"On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model","created":"2023-10-09","cited_by_count":7,"area":null},{"id":"2104.02532","title":"End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning","created":"2021-04-06","cited_by_count":6,"area":null},{"id":"2202.08536","title":"Are There Exceptions to Goodhart's Law? On the Moral Justification of Fairness-Aware Machine Learning","created":"2022-02-17","cited_by_count":6,"area":null},{"id":"2303.08403","title":"DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision","created":"2023-03-15","cited_by_count":6,"area":null},{"id":"2305.13057","title":"Causality-Aided Trade-off Analysis for Machine Learning Fairness","created":"2023-05-22","cited_by_count":6,"area":null},{"id":"2401.07174","title":"On the (In)Compatibility between Group Fairness and Individual Fairness","created":"2024-01-13","cited_by_count":6,"area":null},{"id":"2108.09098","title":"A fuzzy-rough uncertainty measure to discover bias encoded explicitly or implicitly in features of structured pattern classification datasets","created":"2021-08-20","cited_by_count":5,"area":null},{"id":"2209.07047","title":"iFlipper: Label Flipping for Individual Fairness","created":"2022-09-15","cited_by_count":5,"area":null},{"id":"2212.06750","title":"FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data","created":"2022-12-13","cited_by_count":5,"area":null},{"id":"2403.20089","title":"Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness","created":"2024-03-29","cited_by_count":5,"area":null},{"id":"2406.09307","title":"What is Fair? Defining Fairness in Machine Learning for Health","created":"2024-06-13","cited_by_count":5,"area":null},{"id":"2406.17102","title":"Achieving Fairness Across Local and Global Models in Federated Learning","created":"2024-06-24","cited_by_count":5,"area":null},{"id":"2407.11442","title":"EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholders","created":"2024-07-16","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":"2104.03909","title":"RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity","created":"2021-03-16","cited_by_count":4,"area":null},{"id":"2110.02707","title":"Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities","created":"2021-10-06","cited_by_count":4,"area":null},{"id":"2202.04530","title":"An Exploration of Multicalibration Uniform Convergence Bounds","created":"2022-02-09","cited_by_count":4,"area":null},{"id":"2206.00553","title":"FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks","created":"2022-06-01","cited_by_count":4,"area":null},{"id":"2304.09779","title":"Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness","created":"2023-04-19","cited_by_count":4,"area":null},{"id":"2308.08938","title":"Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spaces","created":"2023-08-17","cited_by_count":4,"area":null},{"id":"2310.11407","title":"Group-blind optimal transport to group parity and its constrained variants","created":"2023-10-17","cited_by_count":4,"area":null},{"id":"2504.07395","title":"FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair","created":"2025-04-10","cited_by_count":4,"area":null},{"id":"2102.11932","title":"On Meritocracy in Optimal Set Selection","created":"2021-02-23","cited_by_count":3,"area":null},{"id":"2201.05216","title":"The Fairness Field Guide: Perspectives from Social and Formal Sciences","created":"2022-01-13","cited_by_count":3,"area":null},{"id":"2206.02891","title":"A Justice-Based Framework for the Analysis of Algorithmic Fairness-Utility Trade-Offs","created":"2022-06-06","cited_by_count":3,"area":null},{"id":"2206.02897","title":"Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics","created":"2022-06-06","cited_by_count":3,"area":null},{"id":"2209.01410","title":"Closed-Loop View of the Regulation of AI: Equal Impact across Repeated Interactions","created":"2022-09-03","cited_by_count":3,"area":null},{"id":"2310.18413","title":"On the Fairness ROAD: Robust Optimization for Adversarial Debiasing","created":"2023-10-27","cited_by_count":3,"area":null},{"id":"2406.03292","title":"Evaluating AI fairness in credit scoring with the BRIO tool","created":"2024-06-05","cited_by_count":3,"area":null},{"id":"2504.11504","title":"Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasets","created":"2025-04-15","cited_by_count":3,"area":null},{"id":"2208.08881","title":"Modelling the long-term fairness dynamics of data-driven targeted help on job seekers","created":"2022-08-17","cited_by_count":2,"area":null},{"id":"2208.12786","title":"LUCID: Exposing Algorithmic Bias through Inverse Design","created":"2022-08-26","cited_by_count":2,"area":null},{"id":"2306.14624","title":"Insights From Insurance for Fair Machine Learning","created":"2023-06-26","cited_by_count":2,"area":null},{"id":"2312.01398","title":"Towards Mitigating Perceived Unfairness in Contracts from a Non-Legal Stakeholder's Perspective","created":"2023-12-03","cited_by_count":2,"area":null},{"id":"2405.13693","title":"Mutatis Mutandis: Revisiting the Comparator in Discrimination Testing","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2407.00400","title":"Formalising Anti-Discrimination Law in Automated Decision Systems","created":"2024-06-29","cited_by_count":2,"area":null},{"id":"2504.20642","title":"Decision-centric fairness: Evaluation and optimization for resource allocation problems","created":"2025-04-29","cited_by_count":2,"area":null},{"id":"2508.00138","title":"Co-Producing AI: Toward an Augmented, Participatory Lifecycle","created":"2025-07-31","cited_by_count":2,"area":null},{"id":"2106.06049","title":"FiSH: Fair Spatial Hotspots","created":"2021-06-01","cited_by_count":1,"area":null},{"id":"2207.13596","title":"Fairness and Randomness in Machine Learning: Statistical Independence and Relativization","created":"2022-07-27","cited_by_count":1,"area":null},{"id":"2209.15418","title":"Equitable Marketplace Mechanism Design","created":"2022-09-22","cited_by_count":1,"area":null},{"id":"2302.05906","title":"On Comparing Fair Classifiers under Data Bias","created":"2023-02-12","cited_by_count":1,"area":null},{"id":"2302.07185","title":"When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness","created":"2023-02-14","cited_by_count":1,"area":null},{"id":"2304.06469","title":"Analysing Fairness of Privacy-Utility Mobility Models","created":"2023-04-10","cited_by_count":1,"area":null},{"id":"2304.13493","title":"Towards clinical AI fairness: A translational perspective","created":"2023-04-26","cited_by_count":1,"area":null},{"id":"2307.06513","title":"Leveraging Contextual Counterfactuals Toward Belief Calibration","created":"2023-07-13","cited_by_count":1,"area":null},{"id":"2307.15466","title":"LUCID-GAN: Conditional Generative Models to Locate Unfairness","created":"2023-07-28","cited_by_count":1,"area":null},{"id":"2308.06186","title":"Software Doping Analysis for Human Oversight","created":"2023-08-11","cited_by_count":1,"area":null},{"id":"2401.01262","title":"Fairness Certification for Natural Language Processing and Large Language Models","created":"2024-01-02","cited_by_count":1,"area":null},{"id":"2401.13391","title":"Reranking individuals: The effect of fair classification within-groups","created":"2024-01-24","cited_by_count":1,"area":null},{"id":"2404.13131","title":"From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap","created":"2024-04-19","cited_by_count":1,"area":null},{"id":"2404.18736","title":"Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2407.16895","title":"(Unfair) Norms in Fairness Research: A Meta-Analysis","created":"2024-06-17","cited_by_count":1,"area":null},{"id":"2412.16641","title":"A Systems Thinking Approach to Algorithmic Fairness","created":"2024-12-21","cited_by_count":1,"area":null},{"id":"2502.03038","title":"The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation","created":"2025-02-05","cited_by_count":1,"area":"deeprl"},{"id":"2503.06229","title":"A Frank System for Co-Evolutionary Hybrid Decision-Making","created":"2025-03-08","cited_by_count":1,"area":null},{"id":"2506.11627","title":"Evaluating Fairness and Mitigating Bias in Machine Learning: A Novel Technique using Tensor Data and Bayesian Regression","created":"2025-06-13","cited_by_count":1,"area":null},{"id":"2510.02351","title":"Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs","created":"2025-09-27","cited_by_count":1,"area":"lr"},{"id":"2510.18719","title":"Causally Perturbed Fairness Testing","created":"2025-10-21","cited_by_count":1,"area":null},{"id":"2109.04230","title":"A Systematic Approach to Group Fairness in Automated Decision Making","created":"2021-09-09","cited_by_count":0,"area":null},{"id":"2201.07980","title":"Caring Without Sharing: A Federated Learning Crowdsensing Framework for Diversifying Representation of Cities","created":"2022-01-20","cited_by_count":0,"area":null},{"id":"2202.01013","title":"Fairness of Machine Learning Algorithms in Demography","created":"2022-02-02","cited_by_count":0,"area":null},{"id":"2204.06438","title":"Fair Algorithm Design: Fair and Efficacious Machine Scheduling","created":"2022-04-13","cited_by_count":0,"area":null},{"id":"2209.00617","title":"Fair mapping","created":"2022-09-01","cited_by_count":0,"area":null}],"next_cursor":"100"}