What is new, and what is old, in fairness and machine learning

This article explores the issue of normative distinctiveness in machine learning decision systems alongside Solon Barocas, Moritz Hardt, and Arvind Narayanan's landmark book Fairness and Machine Learning . What, if anything, is different this time, with the rise of machine learning-based aids to bureaucratic decision-making? I show how a focus on normative distinctiveness can obscure from view a much more significant upshot of machine learning: that the mere existence of feasible alternatives presses new justificatory demands not just on the design of new technical systems but on the prevailing human-centered decision regime. I argue that depoliticizing conventional bureaucratic structures of decision-making leads to a missed opportunity for a broader normative reevaluation of what we owe to each other in a world of expanded practical possibility.