SOTAVerified

Fairness

Papers

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Showing 4026–4050 of 5676 papers

TitleStatusHype
Accuracy and Fairness of Facial Recognition Technology in Low-Quality Police Images: An Experiment With Synthetic Faces—0
Accuracy, Fairness, and Interpretability of Machine Learning Criminal Recidivism Models—0
Accuracy vs. Accuracy: Computational Tradeoffs Between Classification Rates and Utility—0
ACE: Adaptive Constraint-aware Early Stopping in Hyperparameter Optimization—0
A Center in Your Neighborhood: Fairness in Facility Location—0
A Characterization of Maximum Nash Welfare for Indivisible Goods—0
Achievement and Fragility of Long-term Equitability—0
Achieving Arbitrary Throughput-Fairness Trade-offs in the Inter Cell Interference Coordination with Fixed Transmit Power Problem—0
Achieving Counterfactual Fairness for Causal Bandit—0
Repairing Regressors for Fair Binary Classification at Any Decision Threshold—0
Achieving Fairness Across Local and Global Models in Federated Learning—0
Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization—0
Achieving Fairness in Multi-Agent Markov Decision Processes Using Reinforcement Learning—0
Achieving Fairness in Predictive Process Analytics via Adversarial Learning—0
Achieving Fairness in the Stochastic Multi-armed Bandit Problem—0
Achieving Fairness with a Simple Ridge Penalty—0
Achieving Fair PCA Using Joint Eigenvalue Decomposition—0
Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects—0
Achieving Long-term Fairness in Submodular Maximization through Randomization—0
Achieving Proportionality up to the Maximin Item with Indivisible Goods—0
Achieving Transparency Report Privacy in Linear Time—0
Achieving User-Side Fairness in Contextual Bandits—0
A Comparison of Differential Performance Metrics for the Evaluation of Automatic Speaker Verification Fairness—0
A Complete Criterion for Value of Information in Soluble Influence Diagrams—0
A Comprehensive Study of Gender Bias in Chemical Named Entity Recognition 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