This paper examines the issue of algorithmic exclusion in bioimpedance-based body composition analysis (BIA) used in smart weight scales, particularly in relation to nonbinary and transgender individuals. The authors, Kendra Albert and Maggie Delano, discuss how the technology’s equations assume a binary sex/gender system, which results in inaccurate measurements and exclusion of those who do not fit into this binary. They also critique the lack of transparency in the equations and the failure to account for sociocultural context.
The authors present a brief background on BIA, including its limitations and critiques. They then dive into the issues surrounding the use of “sex” in BIA equations, highlighting the complexities and entanglements between sex, gender, and biology. They argue that the use of “sex” as a proxy for biological characteristics, such as genitalia, is problematic and that alternative methods of body composition assessment are needed.
The paper also touches on the issues of intersectionality, discussing how individuals with non-normative genders, races, and ethnicities may experience further exclusion and dysaffordance. The authors conclude by emphasizing the need for more inclusive and transparent algorithms in BIA, as well as for greater consideration of the sociocultural context in which the technology is used.
This paper provides a nuanced and thought-provoking exploration of the complex issues surrounding algorithmic exclusion in BIA. It highlights the need for more inclusive and sensitive approaches to body composition analysis, particularly for marginalized communities.
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