Algorithmically encoded identities

Baker, Dylan; Hanna, Alex; Denton, Emily

doi:10.1145/3351095.3375687

Cited by 1 publication

(1 citation statement)

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“…Image tagging systems are often used to sort people into social groups based on their appearance (e.g., gender classification systems), imposing tags on them without their awareness, involvement, or consent. Given the degree to which membership in social groups shapes the way that people are treated within society, including others' expectations of them (Baker, Hanna, and Denton 2020), this imposition can have profound consequences and may cause people to feel like they have been deprived of a crucial aspect of their autonomy (Hanna et al 2020).…”

Section: Denying People the Ability To Self-identifymentioning

confidence: 99%

Taxonomizing and Measuring Representational Harms: A Look at Image Tagging

Katzman

Wang

Scheuerman

et al. 2023

AAAI

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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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Section: Denying People the Ability To Self-identifymentioning

confidence: 99%