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Fairness

Papers

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Showing 5001–5050 of 5676 papers

TitleStatusHype
Differentially Private Fair Learning—0
Differentially Private Tabular Data Synthesis using Large Language Models—0
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey—0
Differential privacy enables fair and accurate AI-based analysis of speech disorders while protecting patient data—0
Differential Privacy, Linguistic Fairness, and Training Data Influence: Impossibility and Possibility Theorems for Multilingual Language Models—0
Differentiated Federated Reinforcement Learning Based Traffic Offloading on Space-Air-Ground Integrated Networks—0
Diffuse-UDA: Addressing Unsupervised Domain Adaptation in Medical Image Segmentation with Appearance and Structure Aligned Diffusion Models—0
Digital Ethics in Federated Learning—0
Dikaios: Privacy Auditing of Algorithmic Fairness via Attribute Inference Attacks—0
Dimensions of Diversity in Human Perceptions of Algorithmic Fairness—0
Diminishing Stereotype Bias in Image Generation Model using Reinforcemenlent Learning Feedback—0
DiRe Committee : Diversity and Representation Constraints in Multiwinner Elections—0
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation—0
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data—0
α-Discounting Multi-Criteria Decision Making (α-D MCDM)—0
Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment—0
Discrimination and Class Imbalance Aware Online Naive Bayes—0
Discrimination-free Insurance Pricing with Privatized Sensitive Attributes—0
Avoiding Disparity Amplification under Different Worldviews—0
Discriminatory Transfer—0
Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design—0
Disentangled Representation Learning with the Gromov-Monge Gap—0
Disentangled Representations from Non-Disentangled Models—0
Disentangled Representation with Causal Constraints for Counterfactual Fairness—0
Disentangling and Operationalizing AI Fairness at LinkedIn—0
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation—0
Dissecting Causal Biases—0
Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies—0
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
Distributed Proximal Policy Optimization for Contention-Based Spectrum Access—0
Distributed Quasi-Newton Method for Fair and Fast Federated Learning—0
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability—0
Distributionally Robust Fair Principal Components via Geodesic Descents—0
Distributionally Robust Federated Learning with Client Drift Minimization—0
Distributionally Robust Optimization with Bias and Variance Reduction—0
Distribution-Free Fair Federated Learning with Small Samples—0
Distributive Fairness in Large Language Models: Evaluating Alignment with Human Values—0
Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?—0
Diverse Client Selection for Federated Learning via Submodular Maximization—0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop—0
Diversity and Inclusion Metrics in Subset Selection—0
Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Software—0
Diversity-Fair Online Selection—0
Diversity in Faces—0
Divide and Conquer: Towards Better Embedding-based Retrieval for Recommender Systems From a Multi-task Perspective—0
Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models—0
DocXplain: A Novel Model-Agnostic Explainability Method for Document Image Classification—0
Does calibration mean what they say it means; or, the reference class problem rises again—0
Does Data-Efficient Generalization Exacerbate Bias in Foundation Models?—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.86—Unverified
21D-CSNNPredictive Equality (age)97.8—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)96.87—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.97—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.45—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)98.68—Unverified
#ModelMetricClaimedVerifiedStatus
11D-CSNNPredictive Equality (age)99.31—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)0.49—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)6.26—Unverified
#ModelMetricClaimedVerifiedStatus
1Neighbour LearningDegree of Bias (DoB)1.96—Unverified