SOTAVerified

Fairness

Papers

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Showing 1476–1500 of 5676 papers

TitleStatusHype
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
Addressing Polarization and Unfairness in Performative Prediction—0
Disentangling and Operationalizing AI Fairness at LinkedIn—0
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors—0
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation—0
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation—0
Coordinated Multi-Armed Bandits for Improved Spatial Reuse in Wi-Fi—0
Bandit-based Communication-Efficient Client Selection Strategies for Federated Learning—0
Ethical AI in Retail: Consumer Privacy and Fairness—0
Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail—0
Dissecting Causal Biases—0
Dissecting Submission Limit in Desk-Rejections: A Mathematical Analysis of Fairness in AI Conference Policies—0
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks—0
Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment—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
AIDRIN 2.0: A Framework to Assess Data Readiness for AI—0
Distributing Synergy Functions: Unifying Game-Theoretic Interaction Methods for Machine-Learning Explainability—0
Cooperative Evolutionary Pressure and Diminishing Returns Might Explain the Fermi Paradox: On What Super-AIs Are Like—0
Distributionally Robust Fair Principal Components via Geodesic Descents—0
Distributionally Robust Federated Learning with Client Drift Minimization—0
Cooperative Energy Scheduling for Microgrids under Peak Demand Energy Plans—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