Taxonomizing and Measuring Representational Harms: A Look at Image Tagging
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In this paper, we examine computational approaches for measuring the "fairness" of image tagging systems, finding that they cluster into five distinct categories, each with its own analytic foundation. We also identify a range of normative concerns that are often collapsed under the terms "unfairness," "bias," or even "discrimination" when discussing problematic cases of image tagging. Specifically, we identify four types of representational harms that can be caused by image tagging systems, providing concrete examples of each. We then consider how different computational measurement approaches map to each of these types, demonstrating that there is not a one-to-one mapping. Our findings emphasize that no single measurement approach will be definitive and that it is not possible to infer from the use of a particular measurement approach which type of harm was intended to be measured. Lastly, equipped with this more granular understanding of the types of representational harms that can be caused by image tagging systems, we show that attempts to mitigate some of these types of harms may be in tension with one another.
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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 [67]
The Introduction (page unavailable) says the paper identifies types of representational harm and evaluates approaches to measuring them, supporting the first part of the claim. The Computational Measurement Approaches section (page unavailable) mentions GAN-generated faces used to test image-tagging systems, but does not describe using poor AI-generated images to elicit feedback from marginalized communities. (LLM, conf=0.88)
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- Authors
- Katzman, J., Wang, A., Scheuerman, M., Blodgett, S. L., Laird, K., Wallach, H., & Barocas, S.
- Affiliations
- University of Michigan (US)
- Princeton University (US)
- University of Colorado Boulder (US)
- Microsoft (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2023-06-26
- Volume
- 37
- Issue
- 12
- Pages
- 14277-14285
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4382469269
- Cited by
- 32
- References
- 58
- Citations by year
- 2026: 8 · 2025: 12 · 2024: 10 · 2023: 2
- FWCI
- 8.02
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- image tagging, representational harm, algorithmic fairness, fairness measurement, bias, discrimination, normative concerns
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