SOTAVerified

Fairness

Papers

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Showing 3001–3025 of 5676 papers

TitleStatusHype
Explainability for identification of vulnerable groups in machine learning models—0
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence—0
Explainable AI for Classifying UTI Risk Groups Using a Real-World Linked EHR and Pathology Lab Dataset—0
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities—0
Explainable AI for Fair Sepsis Mortality Predictive Model—0
Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence—0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction—0
Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review—0
Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research—0
Explainable Deep Reinforcement Learning: State of the Art and Challenges—0
Explainable Disparity Compensation for Efficient Fair Ranking—0
Explainable Fairness in Recommendation—0
Explainable Multi-Stakeholder Job Recommender Systems—0
Explainable post-training bias mitigation with distribution-based fairness metrics—0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments—0
Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition—0
Explaining how your AI system is fair—0
Explaining Knock-on Effects of Bias Mitigation—0
Explaining Language Models' Predictions with High-Impact Concepts—0
Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies—0
Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing—0
Exploiting Cell-Free Massive MIMO for Enabling Simultaneous Wireless Information and Power Transfer—0
Exploiting Fairness to Enhance Sensitive Attributes Reconstruction—0
Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning—0
SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems—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