In a paper presented at the 2020 Conference on Fairness, Accountability, and Transparency in Machine Learning, we describe how and when private companies collect or infer sensitive attribute data, such as a person’s race or ethnicity, for antidiscrimination purposes.
This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities.