A Scoping Review of Gender Stereotypes in Artificial Intelligence

要旨

People often apply gender stereotypes to Artificial Intelligence (AI), and AI design frequently reinforces these stereotypes, perpetuating traditional gender ideologies in state-of-the-art technology. Despite growing interests in investigating this phenomenon, there is little conceptual clarity or consistency regarding what actually constitutes a "gender stereotype" in AI. Therefore, it is critical to provide a more comprehensive image of existing understandings and ongoing discussions of gender stereotypes of AI to guide AI design that reduces the harmful effects of these stereotypes. In doing so, this paper presents a scoping review of over 20 years of research across HCI, HRI and various social science disciplines on how gender stereotypes are applied to AI. We outline the methods and contexts of this growing body of work, develop a typology to clarify these stereotypes, highlight under-explored approaches for future research, and offer guidelines to improve rigor and consistency in this field that may inform responsible AI design in the future.

著者
Wen Duan
Clemson University, Clemson, South Carolina, United States
Lingyuan Li
The University of Texas at Austin, Austin, Texas, United States
Guo Freeman
Clemson University, Clemson, South Carolina, United States
Nathan McNeese
Clemson University , Clemson, South Carolina, United States
DOI

10.1145/3706598.3713093

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713093

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Stereotypes and Gender

G402
7 件の発表
2025-04-29 01:20:00
2025-04-29 02:50:00
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