Individual Fairness, Base Rate Tracking and the Lipschitz Condition

Abstract

In recent years, there has been a proliferation of competing conceptions of what it means for a predictive algorithm to treat its subjects fairly. Most approaches focus on explicating a notion of group fairness, i.e. of what it means for an algorithm to treat one group unfairly in comparison to another. In contrast, Dwork et al. (2012) attempt to carve out a formalised conception of individual fairness, i.e. of what it means for an algorithm to treat an individual fairly or unfairly. In this paper, I demonstrate that the conception of individual fairness advocated by Dwork et al. is closely related to a criterion of group fairness, called ‘base rate tracking’, introduced in Eva (2022). I subsequently show that base rate tracking solves some fundamental conceptual problems associated with the Lipschitz criterion, before arguing that group level fairness criteria are at least as powerful as their individual level counterparts when it comes to diagnosing algorithmic bias.