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Most proposed algorithmic fairness techniques require access to data on a"sensitive attribute" or "protected category" (such as race, ethnicity, gender,or sexuality) in order to make performance comparisons and standardizationsacross groups, however this data is largely unavailable in practice, hinderingthe widespread adoption of algorithmic fairness. Through this paper, weconsider calls to collect more data on demographics to enable algorithmicfairness and challenge the notion that discrimination can be overcome withsmart enough technical methods and sufficient data alone. We show how thesetechniques largely ignore broader questions of data governance and systemicoppression when categorizing individuals for the purpose of fairer algorithmicprocessing. In this work, we explore under what conditions demographic datashould be collected and used to enable algorithmic fairness methods bycharacterizing a range of social risks to individuals and communities. For therisks to individuals we consider the unique privacy risks associated with thesharing of sensitive attributes likely to be the target of fairness analysis,the possible harms stemming from miscategorizing and misrepresentingindividuals in the data collection process, and the use of sensitive databeyond data subjects' expectations. Looking more broadly, the risks to entiregroups and communities include the expansion of surveillance infrastructure inthe name of fairness, misrepresenting and mischaracterizing what it means to bepart of a demographic group or to hold a certain identity, and ceding theability to define for themselves what constitutes biased or unfair treatment.We argue that, by confronting these questions before and during the collectionof demographic data, algorithmic fairness methods are more likely to actuallymitigate harmful treatment disparities without reinforcing systems ofoppression.

Demographic-Reliant Algorithmic Fairness: Characterizing the Risks of Demographic Data Collection in the Pursuit of Fairness