SOTAVerified

Fairness

Papers

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Showing 2401–2425 of 5676 papers

TitleStatusHype
Don't Kill the Baby: The Case for AI in Arbitration—0
Benchmarking Bias in Large Language Models during Role-Playing—0
An Admissible Shift-Consistent Method for Recommender Systems—0
Don't Judge Me by My Face : An Indirect Adversarial Approach to Remove Sensitive Information From Multimodal Neural Representation in Asynchronous Job Video Interviews—0
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
Do Not Harm Protected Groups in Debiasing Language Representation Models—0
A Deep Learning Approach for User-Centric Clustering in Cell-Free Massive MIMO Systems—0
Dominant Resource Fairness with Meta-Types—0
Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition—0
BenchmarkCards: Large Language Model and Risk Reporting—0
Domain Adaptation meets Individual Fairness. And they get along—0
A Multivocal Literature Review on Privacy and Fairness in Federated Learning—0
Do LLM Agents Exhibit Social Behavior?—0
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers—0
Be Intentional About Fairness!: Fairness, Size, and Multiplicity in the Rashomon Set—0
Multi-FR: A Multi-objective Optimization Framework for Multi-stakeholder Fairness-aware Recommendation—0
A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research—0
Achieving Fairness with a Simple Ridge Penalty—0
A Benchmark for Fairness-Aware Graph Learning—0
From Embeddings to Accuracy: Comparing Foundation Models for Radiographic Classification—0
AI for All: Identifying AI incidents Related to Diversity and Inclusion—0
Metric Learning for Individual Fairness—0
Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings—0
Doing Right by Not Doing Wrong in Human-Robot Collaboration—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