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Cited by 6Ethics and Social Impacts of AIHate Speech and Cyberbullying DetectionDiscourse Analysis in Language Studies

Taxonomizing Representational Harms using Speech Act Theory

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Corvi, Emily, Washington, Hannah, Reed, Stefanie, Atalla, Chad, Chouldechova, Alexandra, Dow, P. Alex, Garcia-Gathright, Jean, Pangakis, Nicholas J +4 more·2025·Findings of the Association for Computational Linguistics: ACL 2025
DOI 10.18653/v1/2025.findings-acl.202
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Abstract

Emily Corvi, Hannah Washington, Stefanie Reed, Chad Atalla, Alexandra Chouldechova, P. Alex Dow, Jean Garcia-Gathright, Nicholas J Pangakis, Emily Sheng, Dan Vann, Matthew Vogel, Hanna Wallach. Findings of the Association for Computational Linguistics: ACL 2025. 2025.

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  • Existing research predominantly focuses on understanding, defining or evaluating representational 'harms' and often uses poor AI-generated images to elicit feedback from marginalized communities.

    “Our approach to defining and evaluating 'good' representation differs from existing research that predominantly focuses on understanding, defining or evaluating representational 'harms' [24, 28, 38, 44, 67, 77, 91, 116] and often uses poor AI-generated images to elicit feedback from marginalized communities (e.g., [65, 77, 79]).”

    From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [28]

    The Introduction and Conclusion show that this paper defines and examines representational harms, supporting the first part of the attribution. The Limitations section says it focuses on language generation rather than images; the cited paper does not support the claim that it uses poor AI-generated images to elicit feedback from marginalized communities. (LLM, conf=0.90)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Corvi, E., Washington, H., Reed, S., Atalla, C., Chouldechova, A., Dow, P. A., Garcia-Gathright, J., Pangakis, N. J., Sheng, E., Vann, D., Vogel, M., & Wallach, H.
Affiliations
Microsoft (United States) (US)
ORCID
Venue
Findings of the Association for Computational Linguistics: ACL 2025
Publisher
Association for Computational Linguistics
Published
2025
Pages
3907-3932
DOI
10.18653/v1/2025.findings-acl.202
OpenAlex
W4412888682
Cited by
6
References
0
Citations by year
2026: 5 · 2025: 1
FWCI
6.09
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Social Sciences › Arts and Humanities › Literature and Literary Theory › Discourse Analysis in Language Studies
Keywords
representational harm, speech act theory, algorithmic harms, AI-generated text

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