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The Potential of Diverse Youth as Stakeholders in Identifying and Mitigating Algorithmic Bias for a Future of Fairer AI

Solyst, Jaemarie, Yang, Ellia, Xie, Shixian, Ogan, Amy, Hammer, Jessica, Eslami, Motahhare·2023·Proceedings of the ACM on Human-Computer Interaction
DOI 10.1145/3610213
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Abstract

Youth regularly use technology driven by artificial intelligence (AI). However, it is increasingly well-known that AI can cause harm on small and large scales, especially for those underrepresented in tech fields. Recently, users have played active roles in surfacing and mitigating harm from algorithmic bias. Despite being frequent users of AI, youth have been under-explored as potential contributors and stakeholders to the future of AI. We consider three notions that may be at the root of youth facing barriers to playing an active role in responsible AI, which are youth (1) cannot understand the technical aspects of AI, (2) cannot understand the ethical issues around AI, and (3) need protection from serious topics related to bias and injustice. In this study, we worked with youth (N = 30) in first through twelfth grade and parents (N = 6) to explore how youth can be part of identifying algorithmic bias and designing future systems to address problematic technology behavior. We found that youth are capable of identifying and articulating algorithmic bias, often in great detail. Participants suggested different ways users could give feedback for AI that reflects their values of diversity and inclusion. Youth who may have less experience with computing or exposure to societal structures can be supported by peers or adults with more of this knowledge, leading to critical conversations about fairer AI. This work illustrates youths' insights, suggesting that they should be integrated in building a future of responsible AI.

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  • Solyst et al. (2023) demonstrated that youth, including young people from marginalized backgrounds, can identify algorithmic bias in detail and propose meaningful design solutions.

    “Solyst et al. (2023) demonstrated that youth themselves, including young people from marginalized backgrounds, can identify algorithmic bias in detail and propose meaningful design solutions, suggesting that participatory approaches to AI development can surface problems that developers might otherwise miss.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Solyst et al. (2023)

  • Several reviewed studies showed that young people, including LGBTQ+ youth, can be meaningfully involved in designing the tools intended to serve them.

    “Several studies reviewed here, including those by Liem et al. (2026), Marraccini et al. (2026), and Solyst et al. (2023), showed that young people, LGBTQ+ youth among them, can be meaningfully involved in designing the tools intended to serve them.”

    From Artificial intelligence and immersive technologies for LGBTQ+ youth: a narrative review (2026) · cites this paper as Solyst et al. (2023)

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Metadata

Authors
Solyst, J., Yang, E., Xie, S., Ogan, A., Hammer, J., & Eslami, M.
Affiliations
Carnegie Mellon University, Pittsburgh, PA, USA
ORCID
Venue
Proceedings of the ACM on Human-Computer Interaction
Publisher
Association for Computing Machinery (ACM)
Published
2023-09-28
Volume
7
Issue
CSCW2
Pages
1-27
ISSN
2573-0142
DOI
10.1145/3610213
Cited by
68
References
6
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor, 6 d)
Funders
NSF (1811086)

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