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<p>Artificial intelligence systems increasingly mediate decisions in domains from healthcare and education to law enforcement, but they often inherit historical biases. This paper examines how AI can reproduce and even amplify discrimination at the intersection of transgender identity and neurodivergence. Drawing on evidence that transgender individuals exhibit higher rates of neurodivergence (Pasterski et al. in Arch Sex Behav 43:387–393, 2014) and on the historical pathologization of both identities (Conrad and Schneider in Deviance and medicalization: from badness to sickness, Temple University Press, Philadelphia, 1992), I focus on two domains, healthcare and language processing, to illustrate how choices in data collection, model training, and algorithm design can perpetuate harmful biases. I also evaluate restrictive state policies as exemplified by the U.S. executive order “Defending Women…” (Trump Executive Order. Defending women from gender ideology extremism and restoring biological truth to the federal government. 2025a. <a href="https://www.whitehouse.gov/Presidential-Actions/2025/01/Defending-Women-From-Gender-Ideology-Extremism-And-Restoring-Biological-Truth-To-The-Federal-Government/">https://www.whitehouse.gov/Presidential-Actions/2025/01/Defending-Women-From-Gender-Ideology-Extremism-And-Restoring-Biological-Truth-To-The-Federal-Government/</a>) while exploring how regulatory frameworks may complicate efforts toward inclusive AI design. I conclude with technical and policy recommendations intended to promote fairness while acknowledging the potential benefits and risks of AI systems, and suggest avenues for future research.</p>

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The ethics of AI at the intersection of transgender identity and neurodivergence

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月7日

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  • Max Parks

摘要

Artificial intelligence systems increasingly mediate decisions in domains from healthcare and education to law enforcement, but they often inherit historical biases. This paper examines how AI can reproduce and even amplify discrimination at the intersection of transgender identity and neurodivergence. Drawing on evidence that transgender individuals exhibit higher rates of neurodivergence (Pasterski et al. in Arch Sex Behav 43:387–393, 2014) and on the historical pathologization of both identities (Conrad and Schneider in Deviance and medicalization: from badness to sickness, Temple University Press, Philadelphia, 1992), I focus on two domains, healthcare and language processing, to illustrate how choices in data collection, model training, and algorithm design can perpetuate harmful biases. I also evaluate restrictive state policies as exemplified by the U.S. executive order “Defending Women…” (Trump Executive Order. Defending women from gender ideology extremism and restoring biological truth to the federal government. 2025a. https://www.whitehouse.gov/Presidential-Actions/2025/01/Defending-Women-From-Gender-Ideology-Extremism-And-Restoring-Biological-Truth-To-The-Federal-Government/) while exploring how regulatory frameworks may complicate efforts toward inclusive AI design. I conclude with technical and policy recommendations intended to promote fairness while acknowledging the potential benefits and risks of AI systems, and suggest avenues for future research.