Gender Biases in LLMs: Higher intelligence in LLM does not necessarily solve gender bias and stereotyping

• URL: http://arxiv.org/abs/2409.19959v2
• Date: Sat, 15 Feb 2025 20:01:40 GMT
• Title: Gender Biases in LLMs: Higher intelligence in LLM does not necessarily solve gender bias and stereotyping
• Authors: Rajesh Ranjan, Shailja Gupta, Surya Naranyan Singh,
• Abstract summary: Large Language Models (LLMs) are finding applications in all aspects of life, but their susceptibility to biases, particularly gender stereotyping, raises ethical concerns.<n>This study introduces a novel methodology, a persona-based framework, and a unisex name methodology to investigate whether higher-intelligence LLMs reduce such biases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large Language Models (LLMs) are finding applications in all aspects of life, but their susceptibility to biases, particularly gender stereotyping, raises ethical concerns. This study introduces a novel methodology, a persona-based framework, and a unisex name methodology to investigate whether higher-intelligence LLMs reduce such biases. We analyzed 1400 personas generated by two prominent LLMs, revealing that systematic biases persist even in LLMs with higher intelligence and reasoning capabilities. o1 rated males higher in competency (8.1) compared to females (7.9) and non-binary (7.80). The analysis reveals persistent stereotyping across fields like engineering, data, and technology, where the presence of males dominates. Conversely, fields like design, art, and marketing show a stronger presence of females, reinforcing societal notions that associate creativity and communication with females. This paper suggests future directions to mitigate such gender bias, reinforcing the need for further research to reduce biases and create equitable AI models.

Related papers

• Who Gets the Callback? Generative AI and Gender Bias [0.030693357740321777]
  We find that large language models (LLMs) tend to favor men, especially for higher-wage roles. A comprehensive analysis of linguistic features in job ads reveals strong alignment of model recommendations with traditional gender stereotypes. Our findings highlight how AI-driven hiring may perpetuate biases in the labor market and have implications for fairness and diversity within firms.
  arXiv  Detail & Related papers  (2025-04-30T07:55:52Z)
• Uncovering an Attractiveness Bias in Multimodal Large Language Models: A Case Study with LLaVA [51.590283139444814]
  We study the role that attractiveness plays in the assessments and decisions made by multimodal large language models (MLLMs) Our analysis reveals that attractiveness impacts the decisions made by the MLLM in over 80% of the scenarios. We uncover a gender, age and race bias in 83%, 73% and 57% of the scenarios, respectively.
  arXiv  Detail & Related papers  (2025-04-16T16:02:55Z)
• The LLM Wears Prada: Analysing Gender Bias and Stereotypes through Online Shopping Data [8.26034886618475]
  We investigate whether Large Language Models can predict an individual's gender based solely on online shopping histories. Using a dataset of historical online purchases from users in the United States, we evaluate the ability of six LLMs to classify gender. Results indicate that while models can infer gender with moderate accuracy, their decisions are often rooted in stereotypical associations between product categories and gender.
  arXiv  Detail & Related papers  (2025-04-02T17:56:08Z)
• Popular LLMs Amplify Race and Gender Disparities in Human Mobility [2.601262068492271]
  This study investigates whether large language models (LLMs) exhibit biases in predicting human mobility based on race and gender. We find that LLMs frequently reflect and amplify existing societal biases.
  arXiv  Detail & Related papers  (2024-11-18T19:41:20Z)
• Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs) [82.57490175399693]
  We study gender bias in 22 popular image-to-text vision-language assistants (VLAs) Our results show that VLAs replicate human biases likely present in the data, such as real-world occupational imbalances. To eliminate the gender bias in these models, we find that finetuning-based debiasing methods achieve the best tradeoff between debiasing and retaining performance on downstream tasks.
  arXiv  Detail & Related papers  (2024-10-25T05:59:44Z)
• Evaluating Gender, Racial, and Age Biases in Large Language Models: A Comparative Analysis of Occupational and Crime Scenarios [0.0]
  This paper examines bias in Large Language Models (LLMs) Findings reveal that LLMs often depict female characters more frequently than male ones in various occupations. Efforts to reduce gender and racial bias often lead to outcomes that may over-index one sub-class.
  arXiv  Detail & Related papers  (2024-09-22T20:21:20Z)
• GenderCARE: A Comprehensive Framework for Assessing and Reducing Gender Bias in Large Language Models [73.23743278545321]
  Large language models (LLMs) have exhibited remarkable capabilities in natural language generation, but have also been observed to magnify societal biases. GenderCARE is a comprehensive framework that encompasses innovative Criteria, bias Assessment, Reduction techniques, and Evaluation metrics.
  arXiv  Detail & Related papers  (2024-08-22T15:35:46Z)
• 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)
• Disclosure and Mitigation of Gender Bias in LLMs [64.79319733514266]
  Large Language Models (LLMs) can generate biased responses. We propose an indirect probing framework based on conditional generation. We explore three distinct strategies to disclose explicit and implicit gender bias in LLMs.
  arXiv  Detail & Related papers  (2024-02-17T04:48:55Z)
• The Male CEO and the Female Assistant: Evaluation and Mitigation of Gender Biases in Text-To-Image Generation of Dual Subjects [58.27353205269664]
  We propose the Paired Stereotype Test (PST) framework, which queries T2I models to depict two individuals assigned with male-stereotyped and female-stereotyped social identities. PST queries T2I models to depict two individuals assigned with male-stereotyped and female-stereotyped social identities. Using PST, we evaluate two aspects of gender biases -- the well-known bias in gendered occupation and a novel aspect: bias in organizational power.
  arXiv  Detail & Related papers  (2024-02-16T21:32:27Z)
• AI Gender Bias, Disparities, and Fairness: Does Training Data Matter? [3.509963616428399]
  This study delves into the pervasive issue of gender issues in artificial intelligence (AI) It analyzes more than 1000 human-graded student responses from male and female participants across six assessment items. Results indicate that scoring accuracy for mixed-trained models shows an insignificant difference from either male- or female-trained models.
  arXiv  Detail & Related papers  (2023-12-17T22:37:06Z)
• Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation [64.79319733514266]
  Large Language Models (LLMs) can generate biased and toxic responses. We propose a conditional text generation mechanism without the need for predefined gender phrases and stereotypes.
  arXiv  Detail & Related papers  (2023-11-01T05:31:46Z)
• Gender bias and stereotypes in Large Language Models [0.6882042556551611]
  This paper investigates Large Language Models' behavior with respect to gender stereotypes. We use a simple paradigm to test the presence of gender bias, building on but differing from WinoBias. Our contributions in this paper are as follows: (a) LLMs are 3-6 times more likely to choose an occupation that stereotypically aligns with a person's gender; (b) these choices align with people's perceptions better than with the ground truth as reflected in official job statistics; (d) LLMs ignore crucial ambiguities in sentence structure 95% of the time in our study items, but when explicitly prompted, they recognize
  arXiv  Detail & Related papers  (2023-08-28T22:32:05Z)
• Towards Understanding Gender-Seniority Compound Bias in Natural Language Generation [64.65911758042914]
  We investigate how seniority impacts the degree of gender bias exhibited in pretrained neural generation models. Our results show that GPT-2 amplifies bias by considering women as junior and men as senior more often than the ground truth in both domains. These results suggest that NLP applications built using GPT-2 may harm women in professional capacities.
  arXiv  Detail & Related papers  (2022-05-19T20:05:02Z)
• Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups [0.8594140167290097]
  The aim of this paper is to investigate the differential performance of the gender classification algorithms across gender-race groups. For all the algorithms used, Black females (Black race in general) always obtained the least accuracy rates. Middle Eastern males and Latino females obtained higher accuracy rates most of the time.
  arXiv  Detail & Related papers  (2020-09-24T04:56:10Z)
• Multi-Dimensional Gender Bias Classification [67.65551687580552]
  Machine learning models can inadvertently learn socially undesirable patterns when training on gender biased text. We propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information.
  arXiv  Detail & Related papers  (2020-05-01T21:23:20Z)

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.