SOTAVerified

Fairness

Papers

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Showing 2801–2825 of 5676 papers

TitleStatusHype
Toward Fair Facial Expression Recognition with Improved Distribution Alignment—0
Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems—0
A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends—0
Preserving privacy in domain transfer of medical AI models comes at no performance costs: The integral role of differential privacyCode0
Investigating Practices and Opportunities for Cross-functional Collaboration around AI Fairness in Industry Practice—0
Safety and Fairness for Content Moderation in Generative Models—0
Toward A Logical Theory Of Fairness and Bias—0
Safe Collaborative FilteringCode0
Are fairness metric scores enough to assess discrimination biases in machine learning?—0
Causal Fairness for Outcome Control—0
DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language ModelsCode0
Shedding light on underrepresentation and Sampling Bias in machine learning—0
Reconciling Predictive and Statistical Parity: A Causal Approach—0
A Systematic Literature Review on Client Selection in Federated Learning—0
Soft-prompt Tuning for Large Language Models to Evaluate Bias—0
ICON^2: Reliably Benchmarking Predictive Inequity in Object Detection—0
BeMap: Balanced Message Passing for Fair Graph Neural NetworkCode0
Migrate Demographic Group For Fair GNNs—0
M^3Fair: Mitigating Bias in Healthcare Data through Multi-Level and Multi-Sensitive-Attribute Reweighting MethodCode0
Fair and Robust Estimation of Heterogeneous Treatment Effects for Policy LearningCode0
Improving Fairness and Robustness in End-to-End Speech Recognition through unsupervised clustering—0
Matched Pair Calibration for Ranking Fairness—0
FedVal: Different good or different bad in federated learningCode0
Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning—0
Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records—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