SOTAVerified

Fairness

Papers

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Showing 2501–2525 of 5676 papers

TitleStatusHype
Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparity—0
Adding guardrails to advanced chatbots—0
h2oGPT: Democratizing Large Language ModelsCode6
Interpolating Item and User Fairness in Multi-Sided Recommendations—0
Trustworthy Artificial Intelligence Framework for Proactive Detection and Risk Explanation of Cyber Attacks in Smart Grid—0
Towards Fair and Explainable AI using a Human-Centered AI ApproachCode1
Unprocessing Seven Years of Algorithmic FairnessCode1
On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective—0
Inference-time Stochastic Ranking with Risk Control—0
A Survey of NOMA: State of the Art, Key Techniques, Open Challenges, Security Issues and Future Trends—0
Skellam Rank: Fair Learning to Rank Algorithm Based on Poisson Process and Skellam Distribution for Recommender Systems—0
Toward Fair Facial Expression Recognition with Improved Distribution Alignment—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
Fair yet Asymptotically Equal Collaborative LearningCode1
Toward A Logical Theory Of Fairness and Bias—0
Are fairness metric scores enough to assess discrimination biases in machine learning?—0
Causal Fairness for Outcome Control—0
Reconciling Predictive and Statistical Parity: A Causal Approach—0
Shedding light on underrepresentation and Sampling Bias in machine learning—0
DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language ModelsCode0
Safe Collaborative FilteringCode0
A Systematic Literature Review on Client Selection in Federated Learning—0
Soft-prompt Tuning for Large Language Models to Evaluate Bias—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