"You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations

• URL: http://arxiv.org/abs/2406.12232v1
• Date: Tue, 18 Jun 2024 03:11:43 GMT
• Title: "You Gotta be a Doctor, Lin": An Investigation of Name-Based Bias of Large Language Models in Employment Recommendations
• Authors: Huy Nghiem, John Prindle, Jieyu Zhao, Hal Daumé III,
• Abstract summary: We utilize GPT-3.5-Turbo and Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations.
• Score: 29.183942575629214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Social science research has shown that candidates with names indicative of certain races or genders often face discrimination in employment practices. Similarly, Large Language Models (LLMs) have demonstrated racial and gender biases in various applications. In this study, we utilize GPT-3.5-Turbo and Llama 3-70B-Instruct to simulate hiring decisions and salary recommendations for candidates with 320 first names that strongly signal their race and gender, across over 750,000 prompts. Our empirical results indicate a preference among these models for hiring candidates with White female-sounding names over other demographic groups across 40 occupations. Additionally, even among candidates with identical qualifications, salary recommendations vary by as much as 5% between different subgroups. A comparison with real-world labor data reveals inconsistent alignment with U.S. labor market characteristics, underscoring the necessity of risk investigation of LLM-powered systems.

Related papers

• Evaluation of Bias Towards Medical Professionals in Large Language Models [11.450991679521605]
  GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases when evaluating medical professionals for residency selection. Tests revealed strong preferences towards Hispanic females and Asian males in various specialties.
  arXiv  Detail & Related papers  (2024-06-30T05:55:55Z)
• GenderBias-\emph{VL}: Benchmarking Gender Bias in Vision Language Models via Counterfactual Probing [72.0343083866144]
  This paper introduces the GenderBias-emphVL benchmark to evaluate occupation-related gender bias in Large Vision-Language Models. Using our benchmark, we extensively evaluate 15 commonly used open-source LVLMs and state-of-the-art commercial APIs. Our findings reveal widespread gender biases in existing LVLMs.
  arXiv  Detail & Related papers  (2024-06-30T05:55:15Z)
• Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? [15.362940175441048]
  We examine whether large language models (LLMs) exhibit race- and gender-based name discrimination in hiring decisions. We design a series of templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. By manipulating the applicant's first name, we measure the effect of perceived race, ethnicity, and gender on the probability that the LLM generates an acceptance or rejection email.
  arXiv  Detail & Related papers  (2024-06-15T03:31:16Z)
• The Silicon Ceiling: Auditing GPT's Race and Gender Biases in Hiring [0.9499648210774584]
  We conduct an algorithm audit of race and gender biases in one commonly-used large language model. We find that the model reflects some biases based on stereotypes. Women's resumes had occupations with less experience, while Asian and Hispanic resumes had immigrant markers.
  arXiv  Detail & Related papers  (2024-05-07T15:39:45Z)
• White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
  Language agency is an important aspect of evaluating social biases in texts. Previous research often relies on string-matching techniques to identify agentic and communal words. We introduce the novel Language Agency Bias Evaluation benchmark.
  arXiv  Detail & Related papers  (2024-04-16T12:27:54Z)
• What's in a Name? Auditing Large Language Models for Race and Gender Bias [49.28899492966893]
  We employ an audit design to investigate biases in state-of-the-art large language models, including GPT-4. We find that the advice systematically disadvantages names that are commonly associated with racial minorities and women.
  arXiv  Detail & Related papers  (2024-02-21T18:25:25Z)
• What Do Llamas Really Think? Revealing Preference Biases in Language Model Representations [62.91799637259657]
  Do large language models (LLMs) exhibit sociodemographic biases, even when they decline to respond? We study this research question by probing contextualized embeddings and exploring whether this bias is encoded in its latent representations. We propose a logistic Bradley-Terry probe which predicts word pair preferences of LLMs from the words' hidden vectors.
  arXiv  Detail & Related papers  (2023-11-30T18:53:13Z)
• Are Emily and Greg Still More Employable than Lakisha and Jamal? Investigating Algorithmic Hiring Bias in the Era of ChatGPT [24.496590819263865]
  Large Language Models (LLMs) such as GPT-3.5, Bard, and Claude exhibit applicability across numerous tasks. We evaluate LLMs on two tasks: (1) matching resumes to job categories; and (2) summarizing resumes with employment relevant information. Overall, LLMs are robust across race and gender. They differ in their performance on pregnancy status and political affiliation.
  arXiv  Detail & Related papers  (2023-10-08T12:08:48Z)
• Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models [104.41668491794974]
  We quantify the usage of adjectives and verbs generated by language models surrounding the names of politicians as a function of their gender. We find that while some words such as dead, and designated are associated with both male and female politicians, a few specific words such as beautiful and divorced are predominantly associated with female politicians.
  arXiv  Detail & Related papers  (2021-04-15T15:03:26Z)
• How True is GPT-2? An Empirical Analysis of Intersectional Occupational Biases [50.591267188664666]
  Downstream applications are at risk of inheriting biases contained in natural language models. We analyze the occupational biases of a popular generative language model, GPT-2. For a given job, GPT-2 reflects the societal skew of gender and ethnicity in the US, and in some cases, pulls the distribution towards gender parity.
  arXiv  Detail & Related papers  (2021-02-08T11:10:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.