Discussions of fairness and machine learning have been painting on too small a canvass. My talk aims to broaden the scope of normative discourse about machine learning and algorithmic decision making. Beginning from an understanding of fair cooperation among free and equal persons as a fundamental political value, I argue that concerns about fairness and machine learning need to be expanded in three ways. First, unfairness and discrimination are not only a matter of group subordination. I consider forms of anti-discrimination that are not about disadvantaged groups but about removing barriers to opportunity, and suggest practical implications for algorithmic decisions. Secondly, I underscore the limits of a focus on fair organizational decisions in advancing equality of opportunity. Finally, drawing on Rawls, I present aspects of a fair society that are not simply matters of equal opportunity, and consider some broader, under-explored ramifications of algorithms and AI on societal fairness. Specifically, I suggest the implications that AI deployment at scale has for fair distribution of wealth and resources and fair political liberties.