Community Voice in Policy Action: Leveraging Participatory Research Methods to Address Representational Bias in AI Systems

Large language models (LLMs) are becoming embedded in systems that shape daily life—from content moderation and search results to educational tools and customer service chatbots. Yet as these technologies cross geographical and cultural boundaries, they carry with them a troubling pattern of systematic underrepresentation and erasure of marginalized communities.

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Publication Details

Journal
ACM SIGCAS Computers and Society
Published
2025-12-01
DOI
https://doi.org/10.1145/3798139.3798145
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

Community Voice in Policy Action: Leveraging Participatory Research Methods to Address Representational Bias in AI Systems

Agrima Seth
ACM SIGCAS Computers and Society
Hate Speech and Cyberbullying Detection
article

Community Voice in Policy Action: Leveraging Participatory Research Methods to Address Representational Bias in AI Systems

Agrima Seth
article en

Abstract

Large language models (LLMs) are becoming embedded in systems that shape daily life—from content moderation and search results to educational tools and customer service chatbots. Yet as these technologies cross geographical and cultural boundaries, they carry with them a troubling pattern of systematic underrepresentation and erasure of marginalized communities.

ACM SIGCAS Computers and SocietyVol. 53(2)
University of Michigan (US)
Reduced inequalities
Openalex Percentile: Top 13%
Hate Speech and Cyberbullying Detection
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