BEATS: Bias Evaluation and Assessment Test Suite for Large Language Models
Alok Abhishek, Lisa Erickson, Tushar Bandopadhyay
2025-04-07
Summary
This paper talks about BEATS, a tool to check if AI language models are being unfair or spreading wrong info, like a test that spots if a chatbot favors certain groups or makes up facts.
What's the problem?
Many popular AI models accidentally show unfairness or spread misinformation in their answers, which could lead to bad decisions if used in things like hiring or news.
What's the solution?
BEATS uses 29 different tests to measure unfairness and inaccuracies in AI responses, from gender bias to fake news risks, helping developers find and fix these issues.
Why it matters?
This matters because it helps make AI safer and fairer for everyone, preventing harm in important areas like healthcare, law, and education where biased AI could hurt people.
Abstract
In this research, we introduce BEATS, a novel framework for evaluating Bias, Ethics, Fairness, and Factuality in Large Language Models (LLMs). Building upon the BEATS framework, we present a bias benchmark for LLMs that measure performance across 29 distinct metrics. These metrics span a broad range of characteristics, including demographic, cognitive, and social biases, as well as measures of ethical reasoning, group fairness, and factuality related misinformation risk. These metrics enable a quantitative assessment of the extent to which LLM generated responses may perpetuate societal prejudices that reinforce or expand systemic inequities. To achieve a high score on this benchmark a LLM must show very equitable behavior in their responses, making it a rigorous standard for responsible AI evaluation. Empirical results based on data from our experiment show that, 37.65\% of outputs generated by industry leading models contained some form of bias, highlighting a substantial risk of using these models in critical decision making systems. BEATS framework and benchmark offer a scalable and statistically rigorous methodology to benchmark LLMs, diagnose factors driving biases, and develop mitigation strategies. With the BEATS framework, our goal is to help the development of more socially responsible and ethically aligned AI models.