SOTAVerified

Fairness

Papers

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Showing 2776–2800 of 5676 papers

TitleStatusHype
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout—0
Gender Bias in Transformer Models: A comprehensive survey—0
General Fair Empirical Risk Minimization—0
Defining bias in AI-systems: Biased models are fair models—0
Automatic Essay Scoring in a Brazilian Scenario—0
Defining the Collective Intelligence Supply Chain—0
Information-Theoretic Bias Assessment Of Learned Representations Of Pretrained Face Recognition—0
FairJudge: Trustworthy User Prediction in Rating Platforms—0
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling—0
Flexible text generation for counterfactual fairness probing—0
Flexibly Fair Representation Learning by Disentanglement—0
SGDE: Secure Generative Data Exchange for Cross-Silo Federated Learning—0
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models—0
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks—0
Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport—0
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT—0
FNAS: Uncertainty-Aware Fast Neural Architecture Search—0
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data—0
Demand-Aware Beam Hopping and Power Allocation for Load Balancing in Digital Twin empowered LEO Satellite Networks—0
Formalising Anti-Discrimination Law in Automated Decision Systems—0
Democratize with Care: The need for fairness specific features in user-interface based open source AutoML tools—0
Fair in the Eyes of Others—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms—0
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare—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