Confusing the Map for the Territory
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The limits of datasets for culturally inclusive AI.
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The identity and views of human annotators become embedded in the data, influencing subsequent system behavior.
“In expressing 'their' perspective through data annotations, it important to recognize how the identity and views of human annotators become embedded in the data, influencing subsequent system behavior [51, 52, 90].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [90]
Recent works argue against treatments of representation as a static concept that can be evaluated objectively.
“This echoes recent works [10, 68, 89, 90] that argue against treatments of representation as a static concept that can be evaluated objectively.”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [90]
Researchers have previously problematized anonymous crowdsourced data annotation approaches as being unspecific about who's worldviews and beliefs become manifest in the data.
“Researchers have previously problematized anonymous crowdsourced data annotation approaches [31, 53, 65] as being unspecific about who's worldviews and beliefs become manifest in the data [11, 17, 51, 78, 90, 91].”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [90]
Rather than treating PwD as a singular or universal identity, the authors acknowledge the distinct social identities and intersectionalities within each community.
“Rather than treating PwD as a singular or universal identity -often reinforced by homogenizing labels such as 'dwarfism -we acknowledge the distinct social identities and intersectionalities within each community (cf. [53, 90]).”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [90]
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Cites
The Risk of Racial Bias in Hate Speech Detection
Sap, M., Card, D., Gabriel, S., Choi, Y., & Smith, N. A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1163 · Cited by 817 · see citing works
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Research works printed but not linked: 11
- Agre, P. Computation and Human Experience. Cambridge University Press. (1997).
- Andrews, J. et al. Ethical considerations for responsible data curation. Advances in Neural Information 2. Processing Systems 36 (2023).
- Bergman, S. et al. STELA: A community-centred approach to norm elicitation for AI alignment. Scientific 3. Reports 14, 1 (2024).
- Diaz, M. et al. Crowdworksheets: Accounting for individual and collective identities underlying crowdsourced 4. dataset annotation. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency. (2022).
- In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. (2020).
- Kapania, S., Taylor, A.S., and Wang, D. A hunt for the snark: Annotator diversity in data practices. In 6. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. (2023).
- Naggita, K., LaChance, J., and Xiang, A. Flickr Africa: Examining geo-diversity in large-scale, human-centric 7. visual data. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. (2023).
- Qadri, R. et al. AI's regimes of representation: A community-centered study of text-to-image models in South 8. Asia. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. (2023).
- Selbst, A.D. et al. Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on 10. Fairness, Accountability, and Transparency. (2019).
- Mary L. Gray (mlg@microsoft.com) is a senior principal researcher at Microsoft Research, Cambridge, MA, USA. 2025 ACM Copyright held by the owner/author(s).
- COMMUNICATIONS OF THE ACM
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Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation
Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works
“In line with arguments by [68, 89, 90], such approaches should recognize the inherently subjective meanings and perspectives that shape social concepts like representation, thereby moving beyond singular or objective standards towards embracing the richness of community-specific interpretations .”
Influential
- Authors
- Qadri, R., Madaio, M., & Gray, M. L.
- Affiliations
- Google (United States) (US)
- Microsoft Research New England (United States) (US)
- Venue
- Communications of the ACM
- Published
- 2025-09-25
- Volume
- 68
- Issue
- 10
- Pages
- 32-34
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W4413926646
- Cited by
- 2
- References
- 1
- Citations by year
- 2026: 2
- FWCI
- 2.09
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- cultural data, data limitations, cultural diversity, algorithmic bias
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