StereoWipe is a comprehensive stereotyping evaluation benchmark for Large Language Models, helping developers build more equitable and culturally-aware AI systems.
StereoWipe provides a systematic approach to measuring and mitigating stereotyping in AI models
Evaluate models using multiple metrics including Stereotype Rate (SR), Stereotype Severity Score (SSS), and Category-Specific scores
Leverage state-of-the-art language models to assess stereotyping content with nuanced understanding
Test across 12+ categories including cultural, linguistic, socioeconomic, and intersectional stereotypes
Simple workflow to evaluate your AI model's fairness
Use our curated prompts or bring your own test cases and model responses
Execute the CLI tool with your preferred judge (OpenAI, Anthropic, or Mock)
Review comprehensive metrics and category breakdowns in the web viewer
Use insights to fine-tune and improve your model's fairness
StereoWipe addresses a critical gap in AI evaluation. While current benchmarks often rely on abstract definitions and Western-centric assumptions, we provide a nuanced, globally-aware approach to measuring stereotyping in language models.
Our benchmark empowers developers, researchers, and policymakers to build AI systems that serve all communities equitably, promoting social understanding rather than reinforcing harmful stereotypes.