SOTAVerified

Fairness

Papers

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Showing 1676–1700 of 5676 papers

TitleStatusHype
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection—0
Leveraging vision-language models for fair facial attribute classification—0
Interpretable Machine Learning for Survival AnalysisCode0
Scalable Algorithms for Individual Preference Stable Clustering—0
Impact of Objective Function on Spectral Efficiency in Integrated HAPS-Terrestrial Networks—0
Fairness-Aware Multi-Server Federated Learning Task Delegation over Wireless Networks—0
Pantypes: Diverse Representatives for Self-Explainable ModelsCode0
B-AVIBench: Towards Evaluating the Robustness of Large Vision-Language Model on Black-box Adversarial Visual-InstructionsCode0
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey—0
Evaluating LLMs for Gender Disparities in Notable Persons—0
Developing and Deploying Industry Standards for Artificial Intelligence in Education (AIED): Challenges, Strategies, and Future Directions—0
REFRESH: Responsible and Efficient Feature Reselection Guided by SHAP Values—0
Measures of relevance to the success of streaming platforms—0
Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination CriteriaCode0
Specification Overfitting in Artificial Intelligence—0
AI Ethics: A Bibliometric Analysis, Critical Issues, and Key Gaps—0
Legally Binding but Unfair? Towards Assessing Fairness of Privacy Policies—0
Price Gouging or Market Forces? Fairness Perceptions of Price Hikes in the Pandemic—0
Improving Fairness in Photovoltaic Curtailments via Daily Topology Reconfiguration for Voltage Control in Power Distribution Networks—0
On the Nonconvexity of Push-Forward Constraints and Its Consequences in Machine Learning—0
Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias—0
FairRR: Pre-Processing for Group Fairness through Randomized ResponseCode0
Balancing Fairness and Accuracy in Data-Restricted Binary Classification—0
Don't Forget What I did?: Assessing Client Contributions in Federated Learning—0
Monotone Individual Fairness—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