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Lookup NU author(s): Dr Zachary PetzelORCiD, Leanne Sowerby
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Users report prejudiced responses generated by large language models (LLMs) like ChatGPT. Across 3 preregistered experiments, members of stigmatized social groups (Black Americans, women) reported higher trustworthiness of LLMs after viewing unbiased interactions with ChatGPT compared to when viewing AI-generated prejudice (i.e., racial or gender disparities in salary). Notably, higher trustworthiness accounted for increased behavioral intentions to use LLMs, but only among stigmatized social groups. Conversely, White Americans were more likely to use LLMs when AI-generated prejudice confirmed implicit racial biases, while men intended to use LLMs when responses matched implicit gender biases. Results suggest reducing AI-generated prejudice may promote trustworthiness of LLMs among members of stigmatized social groups, increasing their intentions to use AI tools. Importantly, addressing AI-generated prejudice could minimize social disparities in adoption of LLMs which might further exacerbate professional and educational disparities. Given expected integration of AI in professional and educational settings, these findings may guide equitable implementation strategies among employees and students, in addition to extending theoretical models of technology acceptance by suggesting additional mechanisms of behavioral intentions to use emerging technologies (e.g., trustworthiness).
Author(s): Petzel ZW, Sowerby L
Publication type: Article
Publication status: Published
Journal: Computers in Human Behavior
Year: 2025
Volume: 165
Print publication date: 01/04/2025
Online publication date: 15/01/2025
Acceptance date: 10/01/2025
Date deposited: 22/01/2025
ISSN (print): 0747-5632
ISSN (electronic): 1873-7692
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.chb.2025.108563
DOI: 10.1016/j.chb.2025.108563
Data Access Statement: Data and materials used in the paper are freely available via OSF, with links provided in the text.
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