title: Unboxing Occupational Bias: Grounded Debiasing LLMs with U.S. Labor Data
summary: Large Language Models (LLMs) are prone to inheriting and amplifying societal
  biases embedded within their training data, potentially reinforcing harmful
  stereotypes related to gender, occupation, and other sensitive categories. This
  issue becomes particularly problematic as biased LLMs can have far-reaching
  consequences, leading to unfair practices and exacerbating social inequalities
  across various domains, such as recruitment, online content moderation, or even
  the criminal justice system. Although prior research has focused on detecting
  bias in LLMs using specialized datasets designed to highlight intrinsic biases,
  there has been a notable lack of investigation into how these findings
  correlate with authoritative datasets, such as those from the U.S. National
  Bureau of Labor Statistics (NBLS). To address this gap, we conduct empirical
  research that evaluates LLMs in a ``bias-out-of-the-box" setting, analyzing how
  the generated outputs compare with the distributions found in NBLS data.
  Furthermore, we propose a straightforward yet effective debiasing mechanism
  that directly incorporates NBLS instances to mitigate bias within LLMs. Our
  study spans seven different LLMs, including instructable, base, and
  mixture-of-expert models, and reveals significant levels of bias that are often
  overlooked by existing bias detection techniques. Importantly, our debiasing
  method, which does not rely on external datasets, demonstrates a substantial
  reduction in bias scores, highlighting the efficacy of our approach in creating
  fairer and more reliable LLMs.
paper_authors._id: 66c7c5cbab23d3d7df888a92
paper_authors.name: Atmika Gorti
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-08-22T23:54:40.152Z
paper_authors._id: 658edbf8bec7ad26471d6bd6
paper_authors.avatarUrl: /avatars/5df3438953112018ba6331ce00454878.svg
paper_authors.fullname: Atmika Gorti
paper_authors.type: user
paper_authors.user: atmikag71
paper_authors._id: 66c7c5cbab23d3d7df888a93
paper_authors.name: Manas Gaur
paper_authors._id: 66c7c5cbab23d3d7df888a94
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-26T07:15:53.004Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_title: Unboxing Occupational Bias: Grounded Debiasing LLMs with U.S. Labor Data
paper_summary: Large Language Models (LLMs) are prone to inheriting and amplifying societal
  biases embedded within their training data, potentially reinforcing harmful
  stereotypes related to gender, occupation, and other sensitive categories. This
  issue becomes particularly problematic as biased LLMs can have far-reaching
  consequences, leading to unfair practices and exacerbating social inequalities
  across various domains, such as recruitment, online content moderation, or even
  the criminal justice system. Although prior research has focused on detecting
  bias in LLMs using specialized datasets designed to highlight intrinsic biases,
  there has been a notable lack of investigation into how these findings
  correlate with authoritative datasets, such as those from the U.S. National
  Bureau of Labor Statistics (NBLS). To address this gap, we conduct empirical
  research that evaluates LLMs in a ``bias-out-of-the-box" setting, analyzing how
  the generated outputs compare with the distributions found in NBLS data.
  Furthermore, we propose a straightforward yet effective debiasing mechanism
  that directly incorporates NBLS instances to mitigate bias within LLMs. Our
  study spans seven different LLMs, including instructable, base, and
  mixture-of-expert models, and reveals significant levels of bias that are often
  overlooked by existing bias detection techniques. Importantly, our debiasing
  method, which does not rely on external datasets, demonstrates a substantial
  reduction in bias scores, highlighting the efficacy of our approach in creating
  fairer and more reliable LLMs.
paper_ai_summary: Empirical study evaluates and mitigates bias in large language models using U.S. National Bureau of Labor Statistics data, revealing significant bias reduction through a debiasing mechanism.
paper_ai_keywords: Large Language Models societal biases training data gender occupation recruitment online content moderation criminal justice system bias detection U.S. National Bureau of Labor Statistics bias-out-of-the-box debiasing mechanism instructable models base models mixture-of-expert models