While people in and around the tech industry debate whether algorithms are political at all, social scientists take the politics as a given, asking instead how this politics unfolds: how algorithms concretely govern. What we call “high-tech modernism”- the application of machine learning algorithms to organize our social, economic, and political life-has a dual logic. On the one hand, like traditional bureaucracy, it is an engine of classification, even if it categorizes people and things very differently. On the other, like the market, it provides a means of self-adjusting allocation, though its feedback loops work differently from the price system. Perhaps the most important consequence of high-tech modernism for the contemporary moral political economy is how it weaves hierarchy and data-gathering into the warp and woof of everyday life, replacing visible feedback loops with invisible ones, and suggesting that highly mediated outcomes are in fact the unmediated expression of people's own true wishes.

Grieve Chelwa, Darrick Hamilton, and Avi Green explain how existing accounts of capitalism systematically neglect racial identity group stratification. Their approach points to an important comparative dimension and two significant research agendas that could supplement their arguments. First would be to inquire into the role that equal respect plays in pushing back against stratification. Second would be to investigate how other aspects of social norms may have consequences too, perhaps drawing insights from a new body of research on racial stratification that draws upon Marxian and neoclassical economics.