BEATS: Bias Evaluation and Assessment Test Suite for Large Language
  Models
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.
67ecb513c8ae971f9ad15bd1
Alok Abhishek
extracted_confirmed
2025-04-02T03:59:10.671Z
6478fc1512ae749b62ebbbd5
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Alok Abhishek
user
alokabhishek
67ecb513c8ae971f9ad15bd2
Lisa Erickson
67ecb513c8ae971f9ad15bd3
Tushar Bandopadhyay
extracted_confirmed
2025-04-02T08:13:38.947Z
657372396da136b50f5489a0
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Tushar Bandopadhyay
user
tbandopa
BEATS: Bias Evaluation and Assessment Test Suite for Large Language
  Models
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.
A novel framework, BEATS, evaluates bias, ethics, fairness, and factuality in large language models using a benchmark with 29 diverse metrics.
Large Language Models bias benchmark demographic biases cognitive biases social biases ethical reasoning group fairness factuality misinformation risk responsible AI evaluation rigorous standard mitigation strategies