SOTAVerified

Fairness

Papers

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Showing 626–650 of 5676 papers

TitleStatusHype
FairTTTS: A Tree Test Time Simulation Method for Fairness-Aware ClassificationCode0
The Invisible Hand: Unveiling Provider Bias in Large Language Models for Code Generation—0
Mitigating Algorithmic Bias in Multiclass CNN Classifications Using Causal Modeling—0
Reducing Latency by Eliminating CSIT Feedback: FDD Downlink MIMO Precoding Without CSIT Feedback for Internet-of-Things Communications—0
Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic HiringCode0
Procedural Fairness and Its Relationship with Distributive Fairness in Machine LearningCode0
Bridging the Fairness Gap: Enhancing Pre-trained Models with LLM-Generated Sentences—0
DVM: Towards Controllable LLM Agents in Social Deduction Games—0
Recommending the right academic programs: An interest mining approach using BERTopic—0
Resource Allocation under the Latin Square Constraint—0
Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology courseCode0
On the Sum Rate and User Fairness of STAR-RIS Aided Communications—0
Coverage and Spectral Efficiency of NOMA-Enabled LEO Satellite Networks with Ordering Schemes—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Repeat-bias-aware Optimization of Beyond-accuracy Metrics for Next Basket RecommendationCode0
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform—0
Deontic Temporal Logic for Formal Verification of AI Ethics—0
Recommender Systems for Social Good: The Role of Accountability and Sustainability—0
FairCoder: Evaluating Social Bias of LLMs in Code GenerationCode0
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition BenchmarkCode1
Bundled School Choice—0
Toward Sufficient Statistical Power in Algorithmic Bias Assessment: A Test for ABROCA—0
Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training—0
Who Does the Giant Number Pile Like Best: Analyzing Fairness in Hiring ContextsCode0
BiasGuard: Guardrailing Fairness in Machine Learning Production SystemsCode0
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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