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What ChatGPT Tells Us about Gender: A Cautionary Tale about Performativity and Gender Biases in AI

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  • Nicole Gross

    (National College of Ireland, School of Business, D01Y300 Dublin, Ireland)

Abstract

Large language models and generative AI, such as ChatGPT, have gained influence over people’s personal lives and work since their launch, and are expected to scale even further. While the promises of generative artificial intelligence are compelling, this technology harbors significant biases, including those related to gender. Gender biases create patterns of behavior and stereotypes that put women, men and gender-diverse people at a disadvantage. Gender inequalities and injustices affect society as a whole. As a social practice, gendering is achieved through the repeated citation of rituals, expectations and norms. Shared understandings are often captured in scripts, including those emerging in and from generative AI, which means that gendered views and gender biases get grafted back into social, political and economic life. This paper’s central argument is that large language models work performatively, which means that they perpetuate and perhaps even amplify old and non-inclusive understandings of gender. Examples from ChatGPT are used here to illustrate some gender biases in AI. However, this paper also puts forward that AI can work to mitigate biases and act to ‘undo gender’.

Suggested Citation

  • Nicole Gross, 2023. "What ChatGPT Tells Us about Gender: A Cautionary Tale about Performativity and Gender Biases in AI," Social Sciences, MDPI, vol. 12(8), pages 1-15, August.
    • Handle: RePEc:gam:jscscx:v:12:y:2023:i:8:p:435-:d:1208555
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    References listed on IDEAS

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    1. Simonetta Manfredi & Kate Clayton-Hathway & Emily Cousens, 2019. "Increasing Gender Diversity in Higher Education Leadership: The Role of Executive Search Firms," Social Sciences, MDPI, vol. 8(6), pages 1-17, June.
    2. ., 2023. "The artificial intelligence ecosystem," Chapters, in: The Rise of Algorithmic Society and the Strategic Role of Arts and Culture, chapter 2, pages 6-30, Edward Elgar Publishing.
    3. Jennifer Pabst & Scott M. Walfield & Ryan Schacht, 2022. "Patterning of Sexual Violence against Women across US Cities and Counties," Social Sciences, MDPI, vol. 11(5), pages 1-9, May.
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    Cited by:

    1. Kelvin Leong & Anna Sung, 2024. "Gender stereotypes in artificial intelligence within the accounting profession using large language models," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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