SOTAVerified

Fairness

Papers

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Showing 926–950 of 5676 papers

TitleStatusHype
Client Selection in Federated Learning: Principles, Challenges, and Opportunities—0
Closing the AI Knowledge Gap—0
Cognitive Models as Simulators: The Case of Moral Decision-Making—0
BACSA: A Bias-Aware Client Selection Algorithm for Privacy-Preserving Federated Learning in Wireless Healthcare Networks—0
Achieving Reliable and Fair Skin Lesion Diagnosis via Unsupervised Domain Adaptation—0
Allocating Indivisible Goods to Strategic Agents: Pure Nash Equilibria and Fairness—0
Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?—0
Can everyday AI be ethical. Fairness of Machine Learning Algorithms—0
Can Evolutionary Clustering Have Theoretical Guarantees?—0
Back to the Drawing Board for Fair Representation Learning—0
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML—0
APPLE: Adversarial Privacy-aware Perturbations on Latent Embedding for Unfairness Mitigation—0
Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference—0
Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare—0
Can large language models be privacy preserving and fair medical coders?—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
A Fair and Efficient Hybrid Federated Learning Framework based on XGBoost for Distributed Power Prediction—0
Alleviating the recommendation bias via rank aggregation—0
Can Model Compression Improve NLP Fairness—0
Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness—0
Can Querying for Bias Leak Protected Attributes? Achieving Privacy With Smooth Sensitivity—0
Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models—0
BaBE: Enhancing Fairness via Estimation of Latent Explaining Variables—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