Evaluate AI Fairness with Confidence

StereoWipe is a comprehensive stereotyping evaluation benchmark for Large Language Models, helping developers build more equitable and culturally-aware AI systems.

What We Do

StereoWipe provides a systematic approach to measuring and mitigating stereotyping in AI models

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Comprehensive Metrics

Evaluate models using multiple metrics including Stereotype Rate (SR), Stereotype Severity Score (SSS), and Category-Specific scores

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LLM-as-a-Judge

Leverage state-of-the-art language models to assess stereotyping content with nuanced understanding

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Global Coverage

Test across 12+ categories including cultural, linguistic, socioeconomic, and intersectional stereotypes

How It Works

Simple workflow to evaluate your AI model's fairness

1

Prepare Data

Use our curated prompts or bring your own test cases and model responses

2

Run Evaluation

Execute the CLI tool with your preferred judge (OpenAI, Anthropic, or Mock)

3

Analyze Results

Review comprehensive metrics and category breakdowns in the web viewer

4

Improve Model

Use insights to fine-tune and improve your model's fairness

About StereoWipe

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.

SR
Stereotype Rate - Percentage of stereotyping responses
SSS
Stereotype Severity Score - Average severity of stereotypes
CSSS
Category-Specific Stereotype Severity
WOSI
Weighted Overall Stereotype Index