{"total":715,"rows":[{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436,"area":null},{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207,"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":"2207.07068","title":"Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey","created":"2022-07-14","cited_by_count":125,"area":null},{"id":"2110.00857","title":"FairFed: Enabling Group Fairness in Federated Learning","created":"2021-10-02","cited_by_count":118,"area":null},{"id":"2112.06409","title":"Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective","created":"2021-12-13","cited_by_count":116,"area":null},{"id":"2110.12884","title":"DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks","created":"2021-10-25","cited_by_count":109,"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":"2105.12195","title":"Bias in Machine Learning Software: Why? How? What to do?","created":"2021-05-25","cited_by_count":93,"area":null},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93,"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":"2206.09511","title":"The Fallacy of AI Functionality","created":"2022-06-20","cited_by_count":88,"area":null},{"id":"2206.14754","title":"Distilling Model Failures as Directions in Latent Space","created":"2022-06-29","cited_by_count":82,"area":null},{"id":"2310.12560","title":"Fast Model Debias with Machine Unlearning","created":"2023-10-19","cited_by_count":72,"area":"safety"},{"id":"2104.14210","title":"FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning","created":"2021-04-29","cited_by_count":68,"area":null},{"id":"2205.03257","title":"Synthetic Data -- what, why and how?","created":"2022-05-06","cited_by_count":67,"area":null},{"id":"2101.00352","title":"Characterizing Fairness Over the Set of Good Models Under Selective Labels","created":"2021-01-02","cited_by_count":65,"area":null},{"id":"2103.01907","title":"Fairness in Credit Scoring: Assessment, Implementation and Profit Implications","created":"2021-03-02","cited_by_count":65,"area":null},{"id":"2106.12674","title":"Fairness via Representation Neutralization","created":"2021-06-23","cited_by_count":63,"area":null},{"id":"2101.02032","title":"Socially Responsible AI Algorithms: Issues, Purposes, and Challenges","created":"2021-01-01","cited_by_count":56,"area":null},{"id":"2312.12112","title":"Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes","created":"2023-12-19","cited_by_count":52,"area":null},{"id":"2106.06054","title":"Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline","created":"2021-06-02","cited_by_count":49,"area":null},{"id":"2110.14222","title":"Sample Selection for Fair and Robust Training","created":"2021-10-27","cited_by_count":48,"area":null},{"id":"2202.05338","title":"Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning","created":"2022-02-10","cited_by_count":48,"area":null},{"id":"2204.09274","title":"Causality-based Neural Network Repair","created":"2022-04-20","cited_by_count":47,"area":"safety"},{"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":"2111.14581","title":"Learning Fair Classifiers with Partially Annotated Group Labels","created":"2021-11-29","cited_by_count":40,"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":"2107.10171","title":"Leave-one-out Unfairness","created":"2021-07-21","cited_by_count":36,"area":null},{"id":"2202.04187","title":"FMP: Toward Fair Graph Message Passing against Topology Bias","created":"2022-02-08","cited_by_count":35,"area":null},{"id":"2403.00277","title":"Gender Bias in Large Language Models across Multiple Languages","created":"2024-03-01","cited_by_count":35,"area":null},{"id":"2403.04547","title":"CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?","created":"2024-03-07","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":"2109.00666","title":"TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networks","created":"2021-09-02","cited_by_count":34,"area":null},{"id":"2303.16972","title":"Queer In AI: A Case Study in Community-Led Participatory AI","created":"2023-03-29","cited_by_count":34,"area":null},{"id":"2209.10222","title":"Fairness Reprogramming","created":"2022-09-21","cited_by_count":33,"area":null},{"id":"2306.09468","title":"FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods","created":"2023-06-15","cited_by_count":33,"area":null},{"id":"2107.07754","title":"Measuring Fairness in Generative Models","created":"2021-07-16","cited_by_count":31,"area":null},{"id":"2407.00600","title":"GenderBias-\\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing","created":"2024-06-30","cited_by_count":31,"area":"mllm"},{"id":"2112.09745","title":"Interpretable Data-Based Explanations for Fairness Debugging","created":"2021-12-17","cited_by_count":30,"area":null},{"id":"2207.03277","title":"A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers","created":"2022-07-07","cited_by_count":30,"area":null},{"id":"2304.03722","title":"Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data","created":"2023-04-07","cited_by_count":29,"area":null},{"id":"2104.08769","title":"Fair Representation Learning for Heterogeneous Information Networks","created":"2021-04-18","cited_by_count":28,"area":null},{"id":"2206.00129","title":"Fairness Transferability Subject to Bounded Distribution Shift","created":"2022-05-31","cited_by_count":28,"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":"2205.13619","title":"Fairness in Recommendation: Foundations, Methods and Applications","created":"2022-05-26","cited_by_count":27,"area":null},{"id":"2206.15437","title":"Understanding Instance-Level Impact of Fairness Constraints","created":"2022-06-30","cited_by_count":27,"area":null},{"id":"2212.00926","title":"Fair Generative Models via Transfer Learning","created":"2022-12-02","cited_by_count":27,"area":null},{"id":"2304.09362","title":"Long-Term Fairness with Unknown Dynamics","created":"2023-04-19","cited_by_count":27,"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":"2210.09943","title":"Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognition","created":"2022-10-18","cited_by_count":26,"area":null},{"id":"2306.14123","title":"Privacy and Fairness in Federated Learning: on the Perspective of Trade-off","created":"2023-06-25","cited_by_count":26,"area":null},{"id":"2307.14754","title":"Fair Machine Unlearning: Data Removal while Mitigating Disparities","created":"2023-07-27","cited_by_count":26,"area":"safety"},{"id":"2103.02512","title":"Approximation Algorithms for Socially Fair Clustering","created":"2021-03-03","cited_by_count":25,"area":null},{"id":"2106.02866","title":"Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning","created":"2021-06-05","cited_by_count":25,"area":null},{"id":"2403.01189","title":"Training Unbiased Diffusion Models From Biased Dataset","created":"2024-03-02","cited_by_count":25,"area":"diffusion"},{"id":"2103.00755","title":"Adaptive Sampling for Minimax Fair Classification","created":"2021-03-01","cited_by_count":24,"area":null},{"id":"2109.10431","title":"Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values","created":"2021-09-21","cited_by_count":24,"area":null},{"id":"2202.09724","title":"Bayes-Optimal Classifiers under Group Fairness","created":"2022-02-20","cited_by_count":24,"area":null},{"id":"2209.00427","title":"Fair learning with Wasserstein barycenters for non-decomposable performance measures","created":"2022-09-01","cited_by_count":24,"area":null},{"id":"2106.05964","title":"Fair Classification with Adversarial Perturbations","created":"2021-06-10","cited_by_count":23,"area":null},{"id":"2106.12639","title":"Multi-objective Asynchronous Successive Halving","created":"2021-06-23","cited_by_count":23,"area":null},{"id":"2111.05326","title":"The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning","created":"2021-11-09","cited_by_count":23,"area":null},{"id":"2204.02947","title":"Marrying Fairness and Explainability in Supervised Learning","created":"2022-04-06","cited_by_count":23,"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":"2211.06398","title":"Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach","created":"2022-11-07","cited_by_count":23,"area":null},{"id":"2305.11602","title":"Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing","created":"2023-05-19","cited_by_count":23,"area":null},{"id":"2105.08667","title":"Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency","created":"2021-05-18","cited_by_count":22,"area":null},{"id":"2205.02393","title":"Optimising Equal Opportunity Fairness in Model Training","created":"2022-05-05","cited_by_count":22,"area":null},{"id":"2212.06140","title":"Fairify: Fairness Verification of Neural Networks","created":"2022-12-08","cited_by_count":22,"area":null},{"id":"2406.03198","title":"The Impossibility of Fair LLMs","created":"2024-05-28","cited_by_count":22,"area":null},{"id":"2103.06172","title":"Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems","created":"2021-03-10","cited_by_count":21,"area":null},{"id":"2106.07504","title":"Characterizing the risk of fairwashing","created":"2021-06-14","cited_by_count":21,"area":null},{"id":"2108.02741","title":"GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning","created":"2021-08-05","cited_by_count":21,"area":null},{"id":"2111.04271","title":"Group-Aware Threshold Adaptation for Fair Classification","created":"2021-11-08","cited_by_count":21,"area":null},{"id":"2202.09519","title":"The four-fifths rule is not disparate impact: a woeful tale of epistemic trespassing in algorithmic fairness","created":"2022-02-19","cited_by_count":21,"area":null},{"id":"2204.02567","title":"FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neurons","created":"2022-04-06","cited_by_count":21,"area":null},{"id":"2210.07213","title":"FARE: Provably Fair Representation Learning with Practical Certificates","created":"2022-10-13","cited_by_count":21,"area":null},{"id":"2405.01820","title":"Real Risks of Fake Data: Synthetic Data, Diversity-Washing and Consent Circumvention","created":"2024-05-03","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":"2202.02943","title":"Learning fair representation with a parametric integral probability metric","created":"2022-02-07","cited_by_count":20,"area":null},{"id":"2211.04279","title":"Detecting Shortcuts in Medical Images -- A Case Study in Chest X-rays","created":"2022-11-08","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":"2307.09209","title":"Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models","created":"2023-07-18","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":"2110.01109","title":"FairMask: Better Fairness via Model-based Rebalancing of Protected Attributes","created":"2021-10-03","cited_by_count":19,"area":null},{"id":"2206.02237","title":"Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiency","created":"2022-06-05","cited_by_count":19,"area":null},{"id":"2207.09684","title":"On the Versatile Uses of Partial Distance Correlation in Deep Learning","created":"2022-07-20","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":"2310.06904","title":"Mitigating stereotypical biases in text to image generative systems","created":"2023-10-10","cited_by_count":19,"area":null},{"id":"2103.09055","title":"OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning","created":"2021-03-13","cited_by_count":18,"area":null},{"id":"2105.04273","title":"Loss-Aversively Fair Classification","created":"2021-05-10","cited_by_count":18,"area":null},{"id":"2109.11196","title":"Fast and Efficient MMD-based Fair PCA via Optimization over Stiefel Manifold","created":"2021-09-23","cited_by_count":18,"area":null},{"id":"2111.01201","title":"Unintended Selection: Persistent Qualification Rate Disparities and Interventions","created":"2021-11-01","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":"2302.06333","title":"Improving Recommendation Fairness via Data Augmentation","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2302.06347","title":"The Possibility of Fairness: Revisiting the Impossibility Theorem in Practice","created":"2023-02-13","cited_by_count":18,"area":null},{"id":"2402.18502","title":"Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification","created":"2024-02-28","cited_by_count":18,"area":null},{"id":"2411.12785","title":"Joint Vision-Language Social Bias Removal for CLIP","created":"2024-11-19","cited_by_count":18,"area":null},{"id":"2102.03054","title":"Removing biased data to improve fairness and accuracy","created":"2021-02-05","cited_by_count":17,"area":null}],"next_cursor":"100"}