title: BEATS: Bias Evaluation and Assessment Test Suite for Large Language
    Models
summary: 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.
paper_authors._id: 67ecb513c8ae971f9ad15bd1
paper_authors.name: Alok Abhishek
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-02T03:59:10.671Z
paper_authors._id: 6478fc1512ae749b62ebbbd5
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6478fc1512ae749b62ebbbd5/zw-wYy1vEWnG9-t6c9W_x.jpeg
paper_authors.fullname: Alok Abhishek
paper_authors.type: user
paper_authors.user: alokabhishek
paper_authors._id: 67ecb513c8ae971f9ad15bd2
paper_authors.name: Lisa Erickson
paper_authors._id: 67ecb513c8ae971f9ad15bd3
paper_authors.name: Tushar Bandopadhyay
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-04-02T08:13:38.947Z
paper_authors._id: 657372396da136b50f5489a0
paper_authors.avatarUrl: /avatars/57a693058e9a05a2c32f02bab1d8e819.svg
paper_authors.fullname: Tushar Bandopadhyay
paper_authors.type: user
paper_authors.user: tbandopa
paper_title: BEATS: Bias Evaluation and Assessment Test Suite for Large Language
    Models
paper_summary: 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.
paper_ai_summary: A novel framework, BEATS, evaluates bias, ethics, fairness, and factuality in large language models using a benchmark with 29 diverse metrics.
paper_ai_keywords: 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