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Unveiling Gender Bias in Large Language Models: Using Teacher's Evaluation in Higher Education As an Example

arXiv 2409.09652 ↗First posted 15 Sep 2024cs.CLcited by 0

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Abstract (arXiv)

This paper investigates gender bias in Large Language Model (LLM)-generated teacher evaluations in higher education setting, focusing on evaluations produced by GPT-4 across six academic subjects. By applying a comprehensive analytical framework that includes Odds Ratio (OR) analysis, Word Embedding Association Test (WEAT), sentiment analysis, and contextual analysis, this paper identified patterns of gender-associated language reflecting societal stereotypes. Specifically, words related to approachability and support were used more frequently for female instructors, while words related to entertainment were predominantly used for male instructors, aligning with the concepts of communal and agentic behaviors. The study also found moderate to strong associations between male salient adjectives and male names, though career and family words did not distinctly capture gender biases. These findings align with prior research on societal norms and stereotypes, reinforcing the notion that LLM-generated text reflects existing biases.

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References · 8

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  2. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  3. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  4. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
  5. 2018Gender Bias in Neural Natural Language Processingcited by 210
  6. 2023"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letterscited by 196
  7. 2021Men Are Elected, Women Are Married: Events Gender Bias on Wikipediacited by 30
  8. 2024A survey on fairness of large language models in e-commerce: progress, application, and challengecited by 10

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  1. 2023Gender bias and stereotypes in Large Language Models
  2. 2025Benchmarking Educational LLMs with Analytics: A Case Study on Gender Bias in Feedback
  3. 2025More Women, Same Stereotypes: Unpacking the Gender Bias Paradox in Large Language Models
  4. 2025Explicit vs. Implicit: Investigating Social Bias in Large Language Models through Self-Reflection
  5. 2024White Men Lead, Black Women Help? Benchmarking and Mitigating Language Agency Social Biases in LLMs
  6. 2024Disclosure and Mitigation of Gender Bias in LLMs
  7. 2026Contaminated Collaboration: Measuring Gender Bias Transfer in LLM-Assisted Student Writing
  8. 2025EuroGEST: Investigating gender stereotypes in multilingual language models