{"total":123,"rows":[{"id":"2305.07609","title":"Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation","created":"2023-05-12","cited_by_count":114,"area":null},{"id":"2109.05433","title":"Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search","created":"2021-09-12","cited_by_count":90,"area":null},{"id":"2104.10671","title":"User-oriented Fairness in Recommendation","created":"2021-04-21","cited_by_count":75,"area":null},{"id":"2203.04913","title":"Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers","created":"2022-03-09","cited_by_count":53,"area":null},{"id":"2104.14537","title":"Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features","created":"2021-04-29","cited_by_count":45,"area":null},{"id":"2103.02735","title":"Fairness of Exposure in Stochastic Bandits","created":"2021-03-03","cited_by_count":43,"area":null},{"id":"2105.09829","title":"Personalized Counterfactual Fairness in Recommendation","created":"2021-05-20","cited_by_count":38,"area":null},{"id":"2305.12090","title":"UP5: Unbiased Foundation Model for Fairness-aware Recommendation","created":"2023-05-20","cited_by_count":38,"area":null},{"id":"2206.13102","title":"Modeling Content Creator Incentives on Algorithm-Curated Platforms","created":"2022-06-27","cited_by_count":35,"area":null},{"id":"2106.02705","title":"Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning","created":"2021-06-04","cited_by_count":34,"area":null},{"id":"2102.08410","title":"Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information","created":"2021-02-16","cited_by_count":33,"area":null},{"id":"2209.10117","title":"A Comprehensive Survey on Trustworthy Recommender Systems","created":"2022-09-21","cited_by_count":32,"area":null},{"id":"2104.09024","title":"TFROM: A Two-sided Fairness-Aware Recommendation Model for Both Customers and Providers","created":"2021-04-19","cited_by_count":31,"area":null},{"id":"2205.11127","title":"Fairness in Recommender Systems: Research Landscape and Future Directions","created":"2022-05-23","cited_by_count":30,"area":null},{"id":"2302.03881","title":"On Generalized Degree Fairness in Graph Neural Networks","created":"2023-02-08","cited_by_count":28,"area":null},{"id":"2107.06720","title":"Fairness in Ranking under Uncertainty","created":"2021-07-14","cited_by_count":27,"area":null},{"id":"2205.13619","title":"Fairness in Recommendation: Foundations, Methods and Applications","created":"2022-05-26","cited_by_count":27,"area":null},{"id":"2110.09253","title":"A Sociotechnical View of Algorithmic Fairness","created":"2021-09-27","cited_by_count":24,"area":null},{"id":"2206.03200","title":"FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning","created":"2022-06-07","cited_by_count":23,"area":null},{"id":"2105.02091","title":"When Fair Ranking Meets Uncertain Inference","created":"2021-05-05","cited_by_count":22,"area":null},{"id":"2101.04526","title":"Measuring Recommender System Effects with Simulated Users","created":"2021-01-12","cited_by_count":20,"area":null},{"id":"2305.19329","title":"Mitigating Test-Time Bias for Fair Image Retrieval","created":"2023-05-23","cited_by_count":20,"area":null},{"id":"2110.00151","title":"Lagrangian Inference for Ranking Problems","created":"2021-10-01","cited_by_count":19,"area":null},{"id":"2206.04789","title":"Comprehensive Fair Meta-learned Recommender System","created":"2022-06-09","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":"2111.03015","title":"Modeling Techniques for Machine Learning Fairness: A Survey","created":"2021-11-04","cited_by_count":17,"area":null},{"id":"2404.03192","title":"Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers","created":"2024-04-04","cited_by_count":17,"area":null},{"id":"2202.01906","title":"Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare","created":"2022-02-03","cited_by_count":16,"area":null},{"id":"2208.10451","title":"Minimax AUC Fairness: Efficient Algorithm with Provable Convergence","created":"2022-08-22","cited_by_count":16,"area":null},{"id":"2108.05152","title":"Estimation of Fair Ranking Metrics with Incomplete Judgments","created":"2021-08-11","cited_by_count":14,"area":null},{"id":"2306.00403","title":"A Survey on Fairness-aware Recommender Systems","created":"2023-06-01","cited_by_count":14,"area":null},{"id":"2205.08289","title":"Experiments on Generalizability of User-Oriented Fairness in Recommender Systems","created":"2022-05-17","cited_by_count":13,"area":null},{"id":"2311.14084","title":"Invisible Relevance Bias: Text-Image Retrieval Models Prefer AI-Generated Images","created":"2023-11-23","cited_by_count":13,"area":null},{"id":"2404.01015","title":"PairEval: Open-domain Dialogue Evaluation with Pairwise Comparison","created":"2024-04-01","cited_by_count":11,"area":null},{"id":"2407.18454","title":"Fairness Definitions in Language Models Explained","created":"2024-07-26","cited_by_count":11,"area":null},{"id":"2103.08786","title":"Fairness and Transparency in Recommendation: The Users' Perspective","created":"2021-03-16","cited_by_count":10,"area":null},{"id":"2204.04440","title":"Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks","created":"2022-04-09","cited_by_count":10,"area":null},{"id":"2205.15436","title":"Uncertainty Quantification for Fairness in Two-Stage Recommender Systems","created":"2022-05-30","cited_by_count":10,"area":null},{"id":"2105.02377","title":"Towards Content Provider Aware Recommender Systems: A Simulation Study on the Interplay between User and Provider Utilities","created":"2021-05-06","cited_by_count":9,"area":null},{"id":"2109.08549","title":"Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approach","created":"2021-09-17","cited_by_count":9,"area":null},{"id":"2205.00313","title":"FairSR: Fairness-aware Sequential Recommendation through Multi-Task Learning with Preference Graph Embeddings","created":"2022-04-30","cited_by_count":9,"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":"2407.10853","title":"Bring Your Own Prompts: Use-Case-Specific Bias and Fairness Evaluation for LLMs","created":"2024-07-15","cited_by_count":9,"area":null},{"id":"2102.05996","title":"Fairness Through Regularization for Learning to Rank","created":"2021-02-11","cited_by_count":8,"area":null},{"id":"2204.12176","title":"Cross Pairwise Ranking for Unbiased Item Recommendation","created":"2022-04-26","cited_by_count":8,"area":null},{"id":"2302.01377","title":"Learning with Exposure Constraints in Recommendation Systems","created":"2023-02-02","cited_by_count":8,"area":null},{"id":"2402.02816","title":"Intersectional Two-sided Fairness in Recommendation","created":"2024-02-05","cited_by_count":8,"area":null},{"id":"2504.07801","title":"FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness","created":"2025-04-10","cited_by_count":8,"area":null},{"id":"2105.03153","title":"Pairwise Fairness for Ordinal Regression","created":"2021-05-07","cited_by_count":6,"area":null},{"id":"2112.06288","title":"Fairness for Robust Learning to Rank","created":"2021-12-12","cited_by_count":6,"area":null},{"id":"2505.18532","title":"Preserving AUC Fairness in Learning with Noisy Protected Groups","created":"2025-05-24","cited_by_count":6,"area":null},{"id":"2205.02414","title":"Subverting Fair Image Search with Generative Adversarial Perturbations","created":"2022-05-05","cited_by_count":5,"area":null},{"id":"2205.09240","title":"Debiasing Neural Retrieval via In-batch Balancing Regularization","created":"2022-05-18","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":"2307.14668","title":"Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustment","created":"2023-07-27","cited_by_count":5,"area":null},{"id":"2507.02000","title":"Why Multi-Interest Fairness Matters: Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System","created":"2025-07-01","cited_by_count":5,"area":null},{"id":"2202.13718","title":"Fast Feature Selection with Fairness Constraints","created":"2022-02-28","cited_by_count":4,"area":null},{"id":"2303.13790","title":"Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint","created":"2023-03-24","cited_by_count":4,"area":null},{"id":"2310.06176","title":"Factual and Personalized Recommendations using Language Models and Reinforcement Learning","created":"2023-10-09","cited_by_count":4,"area":null},{"id":"2410.09290","title":"Ranking over Regression for Bayesian Optimization and Molecule Selection","created":"2024-10-11","cited_by_count":4,"area":null},{"id":"2110.15503","title":"A Pre-processing Method for Fairness in Ranking","created":"2021-10-29","cited_by_count":3,"area":null},{"id":"2204.06501","title":"Clinical trial site matching with improved diversity using fair policy learning","created":"2022-04-13","cited_by_count":3,"area":null},{"id":"2304.00295","title":"Fair-CDA: Continuous and Directional Augmentation for Group Fairness","created":"2023-04-01","cited_by_count":3,"area":null},{"id":"2304.07030","title":"FairRec: Fairness Testing for Deep Recommender Systems","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2305.05608","title":"The Role of Relevance in Fair Ranking","created":"2023-05-09","cited_by_count":3,"area":null},{"id":"2306.10050","title":"Interpolating Item and User Fairness in Multi-Sided Recommendations","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2308.11375","title":"Standardized Interpretable Fairness Measures for Continuous Risk Scores","created":"2023-08-22","cited_by_count":3,"area":null},{"id":"2502.13180","title":"Uncertain Multi-Objective Recommendation via Orthogonal Meta-Learning Enhanced Bayesian Optimization","created":"2025-02-18","cited_by_count":3,"area":null},{"id":"2509.08829","title":"PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?","created":"2025-08-20","cited_by_count":3,"area":null},{"id":"2109.05931","title":"FaiREO: User Group Fairness for Equality of Opportunity in Course Recommendation","created":"2021-09-13","cited_by_count":2,"area":null},{"id":"2207.10020","title":"MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Ranking","created":"2022-07-20","cited_by_count":2,"area":null},{"id":"2210.05936","title":"Equal Experience in Recommender Systems","created":"2022-10-12","cited_by_count":2,"area":null},{"id":"2210.09957","title":"Contextual bandits with concave rewards, and an application to fair ranking","created":"2022-10-18","cited_by_count":2,"area":null},{"id":"2212.14351","title":"Properties of Group Fairness Metrics for Rankings","created":"2022-12-29","cited_by_count":2,"area":null},{"id":"2301.07483","title":"Biases in Scholarly Recommender Systems: Impact, Prevalence, and Mitigation","created":"2023-01-18","cited_by_count":2,"area":null},{"id":"2302.00025","title":"On the Within-Group Fairness of Screening Classifiers","created":"2023-01-31","cited_by_count":2,"area":null},{"id":"2302.04119","title":"Local Law 144: A Critical Analysis of Regression Metrics","created":"2023-02-08","cited_by_count":2,"area":null},{"id":"2304.03838","title":"Improving Identity-Robustness for Face Models","created":"2023-04-07","cited_by_count":2,"area":null},{"id":"2304.06596","title":"Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints","created":"2023-04-13","cited_by_count":2,"area":null},{"id":"2305.14396","title":"FITNESS: A Causal De-correlation Approach for Mitigating Bias in Machine Learning Software","created":"2023-05-23","cited_by_count":2,"area":null},{"id":"2306.03775","title":"Matched Pair Calibration for Ranking Fairness","created":"2023-06-06","cited_by_count":2,"area":null},{"id":"2406.05686","title":"Provable Optimization for Adversarial Fair Self-supervised Contrastive Learning","created":"2024-06-09","cited_by_count":2,"area":null},{"id":"2410.02791","title":"DifFaiRec: Generative Fair Recommender with Conditional Diffusion Model","created":"2024-09-18","cited_by_count":2,"area":null},{"id":"2410.18841","title":"From Efficiency to Equity: Measuring Fairness in Preference Learning","created":"2024-10-24","cited_by_count":2,"area":null},{"id":"2411.07589","title":"Overhead-free User-side Recommender Systems","created":"2024-11-12","cited_by_count":2,"area":null},{"id":"2501.04762","title":"Efficient and Responsible Adaptation of Large Language Models for Robust and Equitable Top-k Recommendations","created":"2025-01-08","cited_by_count":2,"area":null},{"id":"2505.12530","title":"Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraints","created":"2025-05-18","cited_by_count":2,"area":null},{"id":"2506.23467","title":"AdFair-CLIP: Adversarial Fair Contrastive Language-Image Pre-training for Chest X-rays","created":"2025-06-30","cited_by_count":2,"area":null},{"id":"2207.04081","title":"Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification","created":"2022-07-08","cited_by_count":1,"area":null},{"id":"2302.02096","title":"Matrix Estimation for Individual Fairness","created":"2023-02-04","cited_by_count":1,"area":null},{"id":"2302.03750","title":"Linking convolutional kernel size to generalization bias in face analysis CNNs","created":"2023-02-07","cited_by_count":1,"area":null},{"id":"2303.15132","title":"Cross-utterance ASR Rescoring with Graph-based Label Propagation","created":"2023-03-27","cited_by_count":1,"area":null},{"id":"2305.15431","title":"Exploring and Exploiting Data Heterogeneity in Recommendation","created":"2023-05-21","cited_by_count":1,"area":null},{"id":"2307.04105","title":"Towards Assumption-free Bias Mitigation","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2307.05728","title":"Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification","created":"2023-07-11","cited_by_count":1,"area":null},{"id":"2309.08375","title":"Boosting Fair Classifier Generalization through Adaptive Priority Reweighing","created":"2023-09-15","cited_by_count":1,"area":null},{"id":"2309.15418","title":"Automatic Feature Fairness in Recommendation via Adversaries","created":"2023-09-27","cited_by_count":1,"area":null},{"id":"2402.05252","title":"Learning Fair Ranking Policies via Differentiable Optimization of Ordered Weighted Averages","created":"2024-02-07","cited_by_count":1,"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":"2502.11429","title":"What's in a Query: Polarity-Aware Distribution-Based Fair Ranking","created":"2025-02-17","cited_by_count":1,"area":null}],"next_cursor":"100"}