SOTAVerified

Fairness

Papers

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Showing 3851–3875 of 5676 papers

TitleStatusHype
Unveiling Pitfalls: Understanding Why AI-driven Code Agents Fail at GitHub Issue Resolution—0
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation—0
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection—0
Unveiling the Impact of Local Homophily on GNN Fairness: In-Depth Analysis and New Benchmarks—0
UPCS: Unbiased Persona Construction for Dialogue Generation—0
Usage Governance Advisor: From Intent to AI Governance—0
Use of a Citizen Science Platform for the Creation of a Language Resource to Study Bias in Language Models for French: A Case Study—0
User and Recommender Behavior Over Time: Contextualizing Activity, Effectiveness, Diversity, and Fairness in Book Recommendation—0
User Fairness in Recommender Systems—0
User-item matching for recommendation fairness—0
User Modeling and User Profiling: A Comprehensive Survey—0
User Pairing and Power Allocation for FTN-based SC-NOMA and MIMO-NOMA Systems Considering User Fairness—0
User Subgrouping and Power Control for Multicast Massive MIMO over Spatially Correlated Channels—0
User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels—0
Using Backbone Foundation Model for Evaluating Fairness in Chest Radiography Without Demographic Data—0
Using ChatGPT to Score Essays and Short-Form Constructed Responses—0
Using Early Readouts to Mediate Featural Bias in Distillation—0
Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations—0
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019—0
Using Pareto Simulated Annealing to Address Algorithmic Bias in Machine Learning—0
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition—0
Using Property Elicitation to Understand the Impacts of Fairness Regularizers—0
Using Protected Attributes to Consider Fairness in Multi-Agent Systems—0
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning—0
Utility Fairness for the Differentially Private Federated Learning—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