SOTAVerified

Bias Detection

Bias detection is the task of detecting and measuring racism, sexism and otherwise discriminatory behavior in a model (Source: https://stereoset.mit.edu/)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 199 papers

TitleStatusHype
How Neural Networks Organize Concepts: Introducing Concept Trajectory Analysis for Deep Learning InterpretabilityCode0
Cascading Adversarial Bias from Injection to Distillation in Language Models—0
Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions—0
Any Large Language Model Can Be a Reliable Judge: Debiasing with a Reasoning-based Bias Detector—0
BiasLab: Toward Explainable Political Bias Detection with Dual-Axis Annotations and Rationale Indicators—0
To Bias or Not to Bias: Detecting bias in News with bias-detectorCode0
Can Global XAI Methods Reveal Injected Bias in LLMs? SHAP vs Rule Extraction vs RuleSHAPCode0
Efficient Fairness Testing in Large Language Models: Prioritizing Metamorphic Relations for Bias Detection—0
Explainable AI in Spatial AnalysisCode2
BiasGuard: A Reasoning-enhanced Bias Detection Tool For Large Language Models—0
Show:102550
← PrevPage 1 of 20Next →
All datasetsStereoSetrt-inod-biasICAT LLM biasPlantVillage_8pxWiki Neutrality Corpus

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RandomForest_default_hyperparametersAccuracy (%)49—Unverified