What is it about?
This study investigates how nonbinary people perceive AI-generated online advertising profiles that categorize users based on inferred gender. Through a mixed-methods survey of 152 nonbinary and gender-nonconforming individuals, the authors examine whether these algorithmic profiles reflect participants’ identities and whether they cause harm. The research is framed within the Human-Computer Interaction (HCI) Grand Challenges, with a particular focus on digital inequality, ethical profiling, and social representation.
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Why is it important?
Digital systems often rely on binary gender assumptions that exclude or misrepresent nonbinary users. This study reveals how AI-generated profiles can contribute to misgendering, privacy violations, and social risk, but also finds that many respondents believe such systems could become more inclusive with the right changes. The findings call attention to urgent design needs, including opt-in classification, user-defined identity fields, and a shift away from gender as a proxy for behavior. By rethinking how we design algorithms and data categories, we can reduce harm and make digital systems more just, respectful, and accurate for everyone.
Perspectives
As a nonbinary researcher and technologist, this project is deeply personal. I've experienced firsthand what it means to be misrepresented by systems that were never built with people like me in mind. This study is part of a broader commitment to equity in tech, not just inclusion as a checkbox, but justice as a design principle. Technology should not erase complexity for the sake of convenience. Our identities are valid, our experiences are real, and we deserve systems that reflect that. By listening to nonbinary voices and elevating our critiques, we can begin reshaping digital spaces to be more humane, ethical, and inclusive.
Dr Sam Leif
Read the Original
This page is a summary of: Exploring Gender Nonbinary Experiences Through the Lens of the 7th HCI Grand Challenge, January 2023, Springer Science + Business Media,
DOI: 10.1007/978-3-031-35989-7_59.
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