SOTAVerified

Fairness

Papers

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Showing 4251–4275 of 5676 papers

TitleStatusHype
An Admissible Shift-Consistent Method for Recommender Systems—0
An Algorithmic Framework to Control Bias in Bandit-based Personalization—0
Analysing a built-in advantage in asymmetric darts contests using causal machine learning—0
Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector—0
Analysing Fairness of Privacy-Utility Mobility Models—0
Analysis of Fairness-promoting Optimization Schemes of Photovoltaic Curtailments for Voltage Regulation in Power Distribution Networks—0
Analysis of Scheduling schemes based on Carrier Aggregation in LTE-Advanced and their improvement—0
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory—0
Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset—0
Analyzing Bias in Sensitive Personal Information Used to Train Financial Models—0
Analyzing Fairness of Classification Machine Learning Model with Structured Dataset—0
Analyzing Fairness of Computer Vision and Natural Language Processing Models—0
Analyzing the Ethical Logic of Six Large Language Models—0
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises—0
An Analysis of the Effects of Decoding Algorithms on Fairness in Open-Ended Language Generation—0
An Automata-Theoretic Approach to Synthesizing Binarized Neural Networks—0
An axiomatic theory for anonymized risk sharing—0
An Axiomatization of the Random Priority Rule—0
"And the Winner Is...": Dynamic Lotteries for Multi-group Fairness-Aware Recommendation—0
An Effective, Robust and Fairness-aware Hate Speech Detection Framework—0
An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee—0
An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret—0
An Empirical Analysis of Fairness Notions under Differential Privacy—0
An Empirical Investigation of Learning from Biased Toxicity Labels—0
An Empirical Study of Accuracy, Fairness, Explainability, Distributional Robustness, and Adversarial Robustness—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