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2022 ACM Conference on Fairness, Accountability, and Transparency by Rieke, Aaron (author);Southerland, Vincent (author);Svirsky, Dan (author);Hsu, Mingwei (author) is a scholarly article available to read on EtoBox.

What is 2022 ACM Conference on Fairness, Accountability, and Transparency about?

Measuring racial disparities is challenging, especially when demographic labels are unavailable. Recently, some researchers and advocates have argued that companies should infer race and other demographic factors to help them understand and address discrimination. Others have been more skeptical, emphasizing the inaccuracy of racial inferences, critiquing the conceptualization of demographic categories themselves, and arguing that the use of demographic data might encourage algorithmic tweaks where more radical interventions are needed. We conduct a novel empirical analysis that informs this debate, using a dataset of self-reported demographic information provided by users of the ride-hailing service Uber who consented to share this information for research purposes. As a threshold matter, we show how this data reflects the enduring power of racism in society. We find differences by race across a range of outcomes. For example, among self-reported African-American riders, we see racial differences on factors from iOS use to local pollution levels. We then turn to a practical assessment of racial inference methodologies and offer two key findings. First, every inference method we te

Author

Rieke, Aaron (author);Southerland, Vincent (author);Svirsky, Dan (author);Hsu, Mingwei (author)

Publisher

ACM

Published

2022

Language

EN