SOTAVerified

Fairness

Papers

Showing 351–375 of 5676 papers

TitleStatusHype
When Counterfactual Reasoning Fails: Chaos and Real-World Complexity—0
Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning ApproachCode0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
The more the merrier: logical and multistage processors in credit scoringCode0
Beyond Detection: Designing AI-Resilient Assessments with Automated Feedback Tool to Foster Critical Thinking—0
A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal ControlCode1
Ethical AI on the Waitlist: Group Fairness Evaluation of LLM-Aided Organ Allocation—0
Fair Sufficient Representation Learning—0
Reproducibility Companion Paper:In-processing User Constrained Dominant Sets for User-Oriented Fairness in Recommender SystemsCode0
Evaluating how LLM annotations represent diverse views on contentious topics—0
Enhancing Federated Learning Through Secure Cluster-Weighted Client Aggregation—0
FairSAM: Fair Classification on Corrupted Data Through Sharpness-Aware Minimization—0
Quantum Doeblin Coefficients: Interpretations and Applications—0
Comparing Methods for Bias Mitigation in Graph Neural Networks—0
Niyama : Breaking the Silos of LLM Inference Serving—0
A Causal Framework to Measure and Mitigate Non-binary Treatment DiscriminationCode0
The Cost of Local and Global Fairness in Federated LearningCode0
NeuroLIP: Interpretable and Fair Cross-Modal Alignment of fMRI and Phenotypic Text—0
FAIR-QR: Enhancing Fairness-aware Information Retrieval through Query Refinement—0
Bias-Aware Agent: Enhancing Fairness in AI-Driven Knowledge RetrievalCode0
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance—0
Active Data Sampling and Generation for Bias Remediation—0
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance—0
Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models—0
FLEX: A Benchmark for Evaluating Robustness of Fairness in Large Language ModelsCode0
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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