{"total":372,"rows":[{"id":"2309.00770","title":"Bias and Fairness in Large Language Models: A Survey","created":"2023-09-02","cited_by_count":639,"area":null},{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436,"area":null},{"id":"2110.00530","title":"A survey on datasets for fairness-aware machine learning","created":"2021-10-01","cited_by_count":157,"area":null},{"id":"2302.08500","title":"Auditing large language models: a three-layered approach","created":"2023-02-16","cited_by_count":137,"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":"2106.00467","title":"A Clarification of the Nuances in the Fairness Metrics Landscape","created":"2021-06-01","cited_by_count":98,"area":null},{"id":"2211.13972","title":"Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?","created":"2022-11-25","cited_by_count":91,"area":null},{"id":"2109.05389","title":"Omnipredictors","created":"2021-09-11","cited_by_count":74,"area":null},{"id":"2209.15145","title":"Batch Multivalid Conformal Prediction","created":"2022-09-30","cited_by_count":67,"area":null},{"id":"2206.01067","title":"Practical Adversarial Multivalid Conformal Prediction","created":"2022-06-02","cited_by_count":65,"area":null},{"id":"2205.04610","title":"Towards Intersectionality in Machine Learning: Including More Identities, Handling Underrepresentation, and Performing Evaluation","created":"2022-05-10","cited_by_count":59,"area":null},{"id":"2211.16886","title":"A Unifying Theory of Distance from Calibration","created":"2022-11-30","cited_by_count":53,"area":null},{"id":"2203.12609","title":"Improving the Fairness of Chest X-ray Classifiers","created":"2022-03-23","cited_by_count":52,"area":null},{"id":"2211.01528","title":"Fair and Optimal Classification via Post-Processing","created":"2022-11-03","cited_by_count":51,"area":null},{"id":"2101.01739","title":"Online Multivalid Learning: Means, Moments, and Prediction Intervals","created":"2021-01-05","cited_by_count":49,"area":null},{"id":"2203.01255","title":"Low-Degree Multicalibration","created":"2022-03-02","cited_by_count":45,"area":null},{"id":"2105.09989","title":"Multi-group Agnostic PAC Learnability","created":"2021-05-20","cited_by_count":44,"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":"2103.05134","title":"Constrained Learning with Non-Convex Losses","created":"2021-03-08","cited_by_count":42,"area":null},{"id":"2305.06969","title":"A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges","created":"2023-05-11","cited_by_count":41,"area":null},{"id":"2310.17651","title":"High-Dimensional Prediction for Sequential Decision Making","created":"2023-10-26","cited_by_count":41,"area":null},{"id":"2102.04257","title":"Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communities","created":"2021-02-03","cited_by_count":40,"area":null},{"id":"2110.15545","title":"Improving Fairness via Federated Learning","created":"2021-10-29","cited_by_count":39,"area":null},{"id":"2202.00787","title":"Achieving Fairness at No Utility Cost via Data Reweighing with Influence","created":"2022-02-01","cited_by_count":38,"area":null},{"id":"2305.18764","title":"When Does Optimizing a Proper Loss Yield Calibration?","created":"2023-05-30","cited_by_count":36,"area":null},{"id":"2205.06922","title":"Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits","created":"2022-05-13","cited_by_count":34,"area":null},{"id":"2210.01745","title":"Making Decisions under Outcome Performativity","created":"2022-10-04","cited_by_count":33,"area":null},{"id":"2201.09051","title":"On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations","created":"2022-01-22","cited_by_count":32,"area":null},{"id":"2210.10837","title":"On Learning Fairness and Accuracy on Multiple Subgroups","created":"2022-10-19","cited_by_count":32,"area":null},{"id":"2302.06726","title":"Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration","created":"2023-02-13","cited_by_count":31,"area":null},{"id":"2109.10441","title":"Evaluating Debiasing Techniques for Intersectional Biases","created":"2021-09-21","cited_by_count":30,"area":null},{"id":"2112.12181","title":"Simple and near-optimal algorithms for hidden stratification and multi-group learning","created":"2021-12-22","cited_by_count":28,"area":null},{"id":"2209.13177","title":"Addressing Fairness Issues in Deep Learning-Based Medical Image Analysis: A Systematic Review","created":"2022-09-27","cited_by_count":28,"area":null},{"id":"2404.15149","title":"Bias patterns in the application of LLMs for clinical decision support: A comprehensive study","created":"2024-04-23","cited_by_count":27,"area":null},{"id":"2101.09385","title":"Outlining Traceability: A Principle for Operationalizing Accountability in Computing Systems","created":"2021-01-23","cited_by_count":26,"area":null},{"id":"2102.12586","title":"A Stochastic Optimization Framework for Fair Risk Minimization","created":"2021-02-24","cited_by_count":26,"area":null},{"id":"2308.05441","title":"Benchmarking Algorithmic Bias in Face Recognition: An Experimental Approach Using Synthetic Faces and Human Evaluation","created":"2023-08-10","cited_by_count":25,"area":null},{"id":"2110.09253","title":"A Sociotechnical View of Algorithmic Fairness","created":"2021-09-27","cited_by_count":24,"area":null},{"id":"2104.14527","title":"Online certification of preference-based fairness for personalized recommender systems","created":"2021-04-29","cited_by_count":22,"area":null},{"id":"2102.04256","title":"Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits","created":"2021-02-03","cited_by_count":21,"area":null},{"id":"2201.10408","title":"An Algorithmic Framework for Bias Bounties","created":"2022-01-25","cited_by_count":21,"area":null},{"id":"2102.06764","title":"Technical Challenges for Training Fair Neural Networks","created":"2021-02-12","cited_by_count":20,"area":null},{"id":"2110.06365","title":"Fast Approximations for Job Shop Scheduling: A Lagrangian Dual Deep Learning Method","created":"2021-10-12","cited_by_count":20,"area":null},{"id":"2205.05770","title":"De-biasing \"bias\" measurement","created":"2022-05-11","cited_by_count":20,"area":null},{"id":"2305.03712","title":"Statistical Inference for Fairness Auditing","created":"2023-05-05","cited_by_count":20,"area":null},{"id":"2106.05127","title":"Deep Clustering based Fair Outlier Detection","created":"2021-06-09","cited_by_count":19,"area":null},{"id":"2301.11781","title":"Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions","created":"2023-01-27","cited_by_count":19,"area":null},{"id":"2304.09424","title":"Loss Minimization Yields Multicalibration for Large Neural Networks","created":"2023-04-19","cited_by_count":19,"area":null},{"id":"2505.21819","title":"Representative Language Generation","created":"2025-05-27","cited_by_count":19,"area":null},{"id":"2111.13650","title":"Latent Space Smoothing for Individually Fair Representations","created":"2021-11-26","cited_by_count":18,"area":null},{"id":"2206.03985","title":"How unfair is private learning ?","created":"2022-06-08","cited_by_count":18,"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":"2303.04379","title":"HappyMap: A Generalized Multi-calibration Method","created":"2023-03-08","cited_by_count":18,"area":null},{"id":"2107.04423","title":"Multiaccurate Proxies for Downstream Fairness","created":"2021-07-09","cited_by_count":17,"area":null},{"id":"2107.08362","title":"Probabilistic Verification of Neural Networks Against Group Fairness","created":"2021-07-18","cited_by_count":17,"area":null},{"id":"2111.03015","title":"Modeling Techniques for Machine Learning Fairness: A Survey","created":"2021-11-04","cited_by_count":17,"area":null},{"id":"2209.07190","title":"Adaptive Fairness Improvement Based on Causality Analysis","created":"2022-09-15","cited_by_count":17,"area":null},{"id":"2211.12703","title":"Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation","created":"2022-11-23","cited_by_count":17,"area":null},{"id":"2310.19691","title":"Causal Context Connects Counterfactual Fairness to Robust Prediction and Group Fairness","created":"2023-10-30","cited_by_count":17,"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":"2210.08781","title":"Stochastic Differentially Private and Fair Learning","created":"2022-10-17","cited_by_count":16,"area":null},{"id":"2105.11069","title":"InfoFair: Information-Theoretic Intersectional Fairness","created":"2021-05-24","cited_by_count":15,"area":null},{"id":"2205.10327","title":"What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment","created":"2022-05-20","cited_by_count":15,"area":null},{"id":"2302.05995","title":"Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview","created":"2023-02-12","cited_by_count":15,"area":null},{"id":"2306.02426","title":"Resilient Constrained Learning","created":"2023-06-04","cited_by_count":15,"area":null},{"id":"2312.11299","title":"Uncertainty-based Fairness Measures","created":"2023-12-18","cited_by_count":15,"area":null},{"id":"2401.14645","title":"Omnipredictors for Regression and the Approximate Rank of Convex Functions","created":"2024-01-26","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":"2209.07312","title":"Multicalibrated Regression for Downstream Fairness","created":"2022-09-15","cited_by_count":14,"area":null},{"id":"2301.08837","title":"From Pseudorandomness to Multi-Group Fairness and Back","created":"2023-01-21","cited_by_count":14,"area":null},{"id":"2405.02225","title":"Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks","created":"2024-05-03","cited_by_count":14,"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":"2205.11584","title":"PrivFairFL: Privacy-Preserving Group Fairness in Federated Learning","created":"2022-05-23","cited_by_count":13,"area":null},{"id":"2208.01182","title":"Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning","created":"2022-08-02","cited_by_count":13,"area":null},{"id":"2307.03201","title":"Scaling Laws Do Not Scale","created":"2023-07-05","cited_by_count":13,"area":null},{"id":"2502.17264","title":"Kandinsky Conformal Prediction: Beyond Class- and Covariate-Conditional Coverage","created":"2025-02-24","cited_by_count":13,"area":null},{"id":"2103.16785","title":"Individually Fair Gradient Boosting","created":"2021-03-31","cited_by_count":12,"area":null},{"id":"2108.12250","title":"A comparison of approaches to improve worst-case predictive model performance over patient subpopulations","created":"2021-08-27","cited_by_count":12,"area":null},{"id":"2201.05893","title":"Treatment Effect Risk: Bounds and Inference","created":"2022-01-15","cited_by_count":12,"area":null},{"id":"2205.04525","title":"Towards a multi-stakeholder value-based assessment framework for algorithmic systems","created":"2022-05-09","cited_by_count":12,"area":null},{"id":"2206.09875","title":"Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models","created":"2022-06-20","cited_by_count":12,"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":"2306.01646","title":"Auditing for Human Expertise","created":"2023-06-02","cited_by_count":12,"area":null},{"id":"2407.13710","title":"OxonFair: A Flexible Toolkit for Algorithmic Fairness","created":"2024-06-30","cited_by_count":12,"area":null},{"id":"2205.11485","title":"Conditional Supervised Contrastive Learning for Fair Text Classification","created":"2022-05-23","cited_by_count":11,"area":null},{"id":"2206.05828","title":"Bounding and Approximating Intersectional Fairness through Marginal Fairness","created":"2022-06-12","cited_by_count":11,"area":null},{"id":"2302.05711","title":"Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLP","created":"2023-02-11","cited_by_count":11,"area":null},{"id":"2103.05853","title":"Multicalibrated Partitions for Importance Weights","created":"2021-03-10","cited_by_count":10,"area":null},{"id":"2205.13740","title":"Subverting machines, fluctuating identities: Re-learning human categorization","created":"2022-05-27","cited_by_count":10,"area":null},{"id":"2209.01215","title":"Exploiting Fairness to Enhance Sensitive Attributes Reconstruction","created":"2022-09-02","cited_by_count":10,"area":null},{"id":"2406.09513","title":"Fair GLASSO: Estimating Fair Graphical Models with Unbiased Statistical Behavior","created":"2024-06-13","cited_by_count":10,"area":null},{"id":"2407.06235","title":"Auditing of AI: Legal, Ethical and Technical Approaches","created":"2024-07-07","cited_by_count":10,"area":null},{"id":"2411.00568","title":"Constrained Sampling with Primal-Dual Langevin Monte Carlo","created":"2024-11-01","cited_by_count":10,"area":null},{"id":"2603.07368","title":"Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairness","created":"2026-03-07","cited_by_count":10,"area":null},{"id":"2107.10960","title":"Implicit Rate-Constrained Optimization of Non-decomposable Objectives","created":"2021-07-23","cited_by_count":9,"area":null},{"id":"2112.02746","title":"Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents","created":"2021-12-06","cited_by_count":9,"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":"2206.00474","title":"Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness","created":"2022-06-01","cited_by_count":9,"area":null}],"next_cursor":"100"}