BEATS: Bias Evaluation and Assessment Test Suite for Large Language
  Models
Authors: name: Alok Abhishek, name: Lisa Erickson, name: Tushar Bandopadhyay

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.