When algorithms cannot be fair: constitutive and contingent discrimination and the limits of less-discriminatory alternatives
The literature on algorithmic fairness has concentrated on the choice amongst fairness metrics, a choice constrained by well-known impossibility results (Chouldechova 2017; Kleinberg et al. 2017). This paper argues that a prior question deserves equal attention: how the categories, variables, and objective functions that constitute an algorithmic system’s representation of its domain are chosen, a