Abstract
In ‘Rawlsian algorithmic fairness and a missing aggregation property of the difference Principle’, the authors argue that there is a false assumption in algorithmic fairness interventions inspired by John Rawls’ theory of justice. They argue that applying the difference principle at the level of a local algorithmic decision-making context (what they term a ‘constituent situation’), is neither necessary nor sufficient for the difference principle to be upheld at the aggregate level of society at large. I find these arguments compelling. They are in line with long-standing concerns about Rawlsian justice in general, and its application in ‘fair’ machine learning in particular. Overall, this article is a very welcome contribution to the literature. In this commentary, I would like to briefly highlight some limitations of these arguments, and some of their broader implications for the field of political philosophy-inspired fair machine learning.