StereoWipe is a research initiative creating comprehensive benchmarks for evaluating bias in Large Language Models, with a focus on subjective cultural assessments across global contexts.
Developing benchmarks to measure stereotyping in Large Language Models with cultural awareness
Large-scale datasets of prompts and responses to evaluate bias across cultural contexts. Our first benchmark evaluates 40+ leading AI models.
State-of-the-art language models assess bias with nuanced understanding, tracking both explicit and implicit stereotyping.
Evaluating bias across Global South and Western contexts with region-specific assessments and cultural norm tracking.
A rigorous approach to stereotyping evaluation
Diverse prompts across 10 bias categories including gender, race, religion, nationality, and profession.
Automated evaluation using Gemini Flash as the primary judge, with human annotation validation.
Human preference voting through side-by-side model comparisons with Elo-based ranking.
Leaderboard refreshed weekly with new evaluations, tracking model improvements over time.
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 biases.