{"total":319,"rows":[{"id":"2312.09390","title":"Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision","created":"2023-12-14","cited_by_count":467,"area":"alignment"},{"id":"2110.01167","title":"Trustworthy AI: From Principles to Practices","created":"2021-10-04","cited_by_count":207,"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":"2110.06733","title":"Systematic Inequalities in Language Technology Performance across the World's Languages","created":"2021-10-13","cited_by_count":120,"area":null},{"id":"2106.08503","title":"Understanding and Evaluating Racial Biases in Image Captioning","created":"2021-06-16","cited_by_count":108,"area":null},{"id":"2203.15046","title":"AUC Maximization in the Era of Big Data and AI: A Survey","created":"2022-03-28","cited_by_count":100,"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":"2106.12674","title":"Fairness via Representation Neutralization","created":"2021-06-23","cited_by_count":63,"area":null},{"id":"2309.12325","title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","created":"2023-08-11","cited_by_count":60,"area":null},{"id":"2106.04411","title":"Fair Feature Distillation for Visual Recognition","created":"2021-05-27","cited_by_count":59,"area":"distill"},{"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":"2101.02032","title":"Socially Responsible AI Algorithms: Issues, Purposes, and Challenges","created":"2021-01-01","cited_by_count":56,"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":"2108.02922","title":"Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Papers","created":"2021-08-06","cited_by_count":49,"area":null},{"id":"2407.00983","title":"FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models","created":"2024-07-01","cited_by_count":47,"area":null},{"id":"2112.13214","title":"NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification","created":"2021-12-25","cited_by_count":42,"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":"2110.15403","title":"Selective Regression Under Fairness Criteria","created":"2021-10-28","cited_by_count":34,"area":null},{"id":"2209.10222","title":"Fairness Reprogramming","created":"2022-09-21","cited_by_count":33,"area":null},{"id":"2302.00799","title":"Charting the Sociotechnical Gap in Explainable AI: A Framework to Address the Gap in XAI","created":"2023-02-01","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":"2108.05449","title":"Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization","created":"2021-08-11","cited_by_count":32,"area":null},{"id":"2104.03483","title":"Question-Driven Design Process for Explainable AI User Experiences","created":"2021-04-08","cited_by_count":31,"area":null},{"id":"2206.14983","title":"A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms","created":"2022-06-30","cited_by_count":30,"area":null},{"id":"2110.06282","title":"The Rich Get Richer: Disparate Impact of Semi-Supervised Learning","created":"2021-10-12","cited_by_count":28,"area":null},{"id":"2402.15350","title":"Farsight: Fostering Responsible AI Awareness During AI Application Prototyping","created":"2024-02-23","cited_by_count":28,"area":null},{"id":"2103.04243","title":"Estimating and Improving Fairness with Adversarial Learning","created":"2021-03-07","cited_by_count":26,"area":null},{"id":"2307.04138","title":"On The Impact of Machine Learning Randomness on Group Fairness","created":"2023-07-09","cited_by_count":26,"area":null},{"id":"2209.07850","title":"FairGBM: Gradient Boosting with Fairness Constraints","created":"2022-09-16","cited_by_count":25,"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":"2310.00566","title":"Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models","created":"2023-10-01","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":"2110.15499","title":"UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Models","created":"2021-10-29","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":"2105.02091","title":"When Fair Ranking Meets Uncertain Inference","created":"2021-05-05","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":"2205.05770","title":"De-biasing \"bias\" measurement","created":"2022-05-11","cited_by_count":20,"area":null},{"id":"2303.00442","title":"Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization","created":"2023-03-01","cited_by_count":20,"area":null},{"id":"2309.08836","title":"Bias and Fairness in Chatbots: An Overview","created":"2023-09-16","cited_by_count":20,"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":"2301.11781","title":"Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions","created":"2023-01-27","cited_by_count":19,"area":null},{"id":"2107.01131","title":"Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization","created":"2021-07-02","cited_by_count":18,"area":null},{"id":"2108.05935","title":"Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets","created":"2021-08-12","cited_by_count":18,"area":null},{"id":"2112.05700","title":"A Framework for Fairness: A Systematic Review of Existing Fair AI Solutions","created":"2021-12-10","cited_by_count":18,"area":null},{"id":"2208.05126","title":"D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias","created":"2022-08-10","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":"2307.03195","title":"A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics","created":"2023-07-03","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},{"id":"2102.05975","title":"Investigating Trade-offs in Utility, Fairness and Differential Privacy in Neural Networks","created":"2021-02-11","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":"2212.04593","title":"Towards Understanding Fairness and its Composition in Ensemble Machine Learning","created":"2022-12-08","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":"2109.09447","title":"Algorithmic Fairness Verification with Graphical Models","created":"2021-09-20","cited_by_count":16,"area":null},{"id":"2110.10200","title":"fairadapt: Causal Reasoning for Fair Data Pre-processing","created":"2021-10-19","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":"2206.07801","title":"Beyond Adult and COMPAS: Fairness in Multi-Class Prediction","created":"2022-06-15","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":"2102.06761","title":"MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset","created":"2021-02-12","cited_by_count":15,"area":null},{"id":"2208.00781","title":"Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods","created":"2022-07-26","cited_by_count":15,"area":null},{"id":"2208.12606","title":"Pushing the limits of fairness impossibility: Who's the fairest of them all?","created":"2022-08-24","cited_by_count":15,"area":null},{"id":"2310.14607","title":"Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications","created":"2023-10-23","cited_by_count":15,"area":"lr"},{"id":"2404.05779","title":"Data Readiness for AI: A 360-Degree Survey","created":"2024-04-08","cited_by_count":15,"area":null},{"id":"2407.18745","title":"FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications","created":"2024-07-26","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":"2201.06952","title":"Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems","created":"2022-01-10","cited_by_count":14,"area":null},{"id":"2302.06975","title":"A Review of the Role of Causality in Developing Trustworthy AI Systems","created":"2023-02-14","cited_by_count":14,"area":null},{"id":"2103.16714","title":"Statistical inference for individual fairness","created":"2021-03-30","cited_by_count":13,"area":null},{"id":"2109.00173","title":"FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes","created":"2021-09-01","cited_by_count":13,"area":null},{"id":"2205.04790","title":"Don't Throw it Away! The Utility of Unlabeled Data in Fair Decision Making","created":"2022-05-10","cited_by_count":13,"area":null},{"id":"2304.10819","title":"Auditing and Generating Synthetic Data with Controllable Trust Trade-offs","created":"2023-04-21","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":"2105.04144","title":"Transitioning from Real to Synthetic data: Quantifying the bias in model","created":"2021-05-10","cited_by_count":12,"area":null},{"id":"2105.14844","title":"Demographic Fairness in Biometric Systems: What do the Experts say?","created":"2021-05-31","cited_by_count":12,"area":null},{"id":"2106.07057","title":"FairCanary: Rapid Continuous Explainable Fairness","created":"2021-06-13","cited_by_count":12,"area":null},{"id":"2112.07467","title":"AI Ethics Principles in Practice: Perspectives of Designers and Developers","created":"2021-12-14","cited_by_count":12,"area":null},{"id":"2201.05759","title":"FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes","created":"2022-01-15","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":"2110.14336","title":"Feature and Label Embedding Spaces Matter in Addressing Image Classifier Bias","created":"2021-10-27","cited_by_count":11,"area":null},{"id":"2202.10848","title":"Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals","created":"2022-02-22","cited_by_count":11,"area":null},{"id":"2204.09389","title":"Epistemic Uncertainty-Weighted Loss for Visual Bias Mitigation","created":"2022-04-20","cited_by_count":11,"area":null},{"id":"2206.00074","title":"To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontier","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2206.11423","title":"Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing","created":"2022-06-22","cited_by_count":11,"area":null},{"id":"2211.00168","title":"Improving Fairness in Image Classification via Sketching","created":"2022-10-31","cited_by_count":11,"area":null},{"id":"2311.04550","title":"Regression with Cost-based Rejection","created":"2023-11-08","cited_by_count":11,"area":null},{"id":"2109.12422","title":"Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models","created":"2021-09-25","cited_by_count":10,"area":null},{"id":"2205.08383","title":"Bias and Fairness on Multimodal Emotion Detection Algorithms","created":"2022-05-11","cited_by_count":10,"area":null},{"id":"2207.06084","title":"Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning","created":"2022-07-13","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":"2307.10223","title":"Bound by the Bounty: Collaboratively Shaping Evaluation Processes for Queer AI Harms","created":"2023-07-15","cited_by_count":10,"area":null},{"id":"2401.08066","title":"Achieve Fairness without Demographics for Dermatological Disease Diagnosis","created":"2024-01-16","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":"2109.12176","title":"On the Fairness of Swarm Learning in Skin Lesion Classification","created":"2021-09-24","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},{"id":"2306.09297","title":"Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML","created":"2023-06-15","cited_by_count":9,"area":null},{"id":"2307.02726","title":"Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matching","created":"2023-07-06","cited_by_count":9,"area":null},{"id":"2402.09683","title":"Exploring a Behavioral Model of \"Positive Friction\" in Human-AI Interaction","created":"2024-02-15","cited_by_count":9,"area":null},{"id":"2404.13060","title":"The Necessity of AI Audit Standards Boards","created":"2024-04-11","cited_by_count":9,"area":null},{"id":"2406.01943","title":"Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs","created":"2024-06-04","cited_by_count":9,"area":null},{"id":"2406.19256","title":"AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI","created":"2024-06-27","cited_by_count":9,"area":null}],"next_cursor":"100"}