SOTAVerified

Fairness

Papers

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Showing 2701–2725 of 5676 papers

TitleStatusHype
FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Fault Diagnosis of Discrete-Event Systems under Non-Deterministic Observations with Output Fairness—0
FedDriveScore: Federated Scoring Driving Behavior with a Mixture of Metric Distributions—0
FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning—0
Entropy-driven Fair and Effective Federated Learning—0
Debias the Black-box: A Fair Ranking Framework via Knowledge Distillation—0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications—0
Algorithmic Fairness and Statistical Discrimination—0
Ad Auctions for LLMs via Retrieval Augmented Generation—0
A3:Ambiguous Aberrations Captured via Astray-Learning for Facial Forgery Semantic Sublimation—0
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps—0
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems—0
Debiasing Pretrained Text Encoders by Paying Attention to Paying Attention—0
FATE in AI: Towards Algorithmic Inclusivity and Accessibility—0
Federated Learning with Fair Worker Selection: A Multi-Round Submodular Maximization Approach—0
Debiasing Neural Retrieval via In-batch Balancing Regularization—0
A Unified Deterministic Channel Model for Multi-Type RIS with Reflective, Transmissive, and Polarization Operations—0
Fast solution to the fair ranking problem using the Sinkhorn algorithm—0
Fast online ranking with fairness of exposure—0
Fast MNAS: Uncertainty-aware Neural Architecture Search with Lifelong Learning—0
Fast Federated Edge Learning with Overlapped Communication and Computation and Channel-Aware Fair Client Scheduling—0
Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Survey—0
Algorithmic Fairness and Social Welfare—0
Fast Feature Selection with Fairness Constraints—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