  Organizations cannot address demographic disparities that they cannot see.
Recent research on machine learning and fairness has emphasized that awareness
of sensitive attributes, such as race and sex, is critical to the development
of interventions. However, on the ground, the existence of these data cannot be
taken for granted.
  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.
  This story has important implications for fairness research and its future
applications. If companies that mediate access to life opportunities are unable
or hesitant to collect or infer sensitive attribute data, then proposed
techniques to detect and mitigate bias in machine learning models might never
be implemented outside the lab. We conclude that today's legal requirements and
corporate practices, while highly inconsistent across domains, offer lessons
for how to approach the collection and inference of sensitive data in
appropriate circumstances. We urge stakeholders, including machine learning
practitioners, to actively help chart a path forward that takes both policy
goals and technical needs into account.
