  Recent work in algorithmic fairness has highlighted the challenge of defining
racial categories for the purposes of anti-discrimination. These challenges are
not new but have previously fallen to the state, which enacts race through
government statistics, policies, and evidentiary standards in
anti-discrimination law. Drawing on the history of state race-making, we
examine how longstanding questions about the nature of race and discrimination
appear within the algorithmic fairness literature. Through a content analysis
of 60 papers published at FAccT between 2018 and 2020, we analyze how race is
conceptualized and formalized in algorithmic fairness frameworks. We note that
differing notions of race are adopted inconsistently, at times even within a
single analysis. We also explore the institutional influences and values
associated with these choices. While we find that categories used in
algorithmic fairness work often echo legal frameworks, we demonstrate that
values from academic computer science play an equally important role in the
construction of racial categories. Finally, we examine the reasoning behind
different operationalizations of race, finding that few papers explicitly
describe their choices and even fewer justify them. We argue that the
construction of racial categories is a value-laden process with significant
social and political consequences for the project of algorithmic fairness. The
widespread lack of justification around the operationalization of race reflects
institutional norms that allow these political decisions to remain obscured
within the backstage of knowledge production.
