SOTAVerified

Fairness

Papers

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Showing 2651–2675 of 5676 papers

TitleStatusHype
Accuracy on the Curve: On the Nonlinear Correlation of ML Performance Between Data SubpopulationsCode0
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability—0
MLHOps: Machine Learning for Healthcare Operations—0
Deep Learning Aided Beamforming for Downlink Non-Orthogonal Multiple Access Systems—0
Recent Advances in the Foundations and Applications of Unbiased Learning to Rank—0
Maximizing Submodular Functions for Recommendation in the Presence of BiasesCode0
Explaining Language Models' Predictions with High-Impact Concepts—0
Fairness in AI Systems: Mitigating gender bias from language-vision models—0
fairml: A Statistician's Take on Fair Machine Learning Modelling—0
Are demographically invariant models and representations in medical imaging fair?—0
On the Impact of Data Quality on Image Classification Fairness—0
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation—0
Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy—0
Multidimensional Fairness in Paper Recommendation—0
Non-Orthogonal Multiple Access Assisted by Reconfigurable Intelligent Surface Using Unsupervised Machine LearningCode0
Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and EquityCode0
Optimizing Privacy, Utility and Efficiency in Constrained Multi-Objective Federated Learning—0
Learning to Re-rank with Constrained Meta-Optimal Transport—0
Visualizing chest X-ray dataset biases using GANs—0
Social Preferences and Deliberately Stochastic Behavior—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
How to address monotonicity for model risk management?—0
Why not both? Complementing explanations with uncertainty, and the role of self-confidence in Human-AI collaboration—0
Oversampling Higher-Performing Minorities During Machine Learning Model Training Reduces Adverse Impact Slightly but Also Reduces Model Accuracy—0
Fairness Uncertainty Quantification: How certain are you that the model is fair?—0
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All datasetsBAF – BaseBAF – Variant IBAF – Variant IIBAF – Variant IIIBAF – Variant IVBAF – Variant VDiveFaceMORPHUTKFace

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