SOTAVerified

Fairness

Papers

Showing 301–325 of 5676 papers

TitleStatusHype
Multi-Stream Transmission in Cell-Free MIMO Networks with Coherent AP Clustering—0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives—0
Diversity-Fair Online Selection—0
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training—0
The Other Side of the Coin: Exploring Fairness in Retrieval-Augmented GenerationCode0
Beyond Global Metrics: A Fairness Analysis for Interpretable Voice Disorder Detection Systems—0
seeBias: A Comprehensive Tool for Assessing and Visualizing AI FairnessCode0
MALIBU Benchmark: Multi-Agent LLM Implicit Bias Uncovered—0
Adaptive Bounded Exploration and Intermediate Actions for Data DebiasingCode0
FAIR-SIGHT: Fairness Assurance in Image Recognition via Simultaneous Conformal Thresholding and Dynamic Output Repair—0
Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable MetricCode1
Enhancements for Developing a Comprehensive AI Fairness Assessment Standard—0
Benchmarking Adversarial Robustness to Bias Elicitation in Large Language Models: Scalable Automated Assessment with LLM-as-a-JudgeCode0
FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness—0
Trustworthy AI Must Account for Intersectionality—0
Deep Fair Learning: A Unified Framework for Fine-tuning Representations with Sufficient Networks—0
Reasoning Towards Fairness: Mitigating Bias in Language Models through Reasoning-Guided Fine-TuningCode0
On the merit principle in strategic exchange—0
Uncovering Fairness through Data Complexity as an Early Indicator—0
Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language ModelsCode2
Investigating Popularity Bias Amplification in Recommender Systems Employed in the Entertainment Domain—0
From Fairness to Truthfulness: Rethinking Data Valuation Design—0
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
HypRL: Reinforcement Learning of Control Policies for Hyperproperties—0
Measuring the right thing: justifying metrics in AI impact assessments—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