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Kiviat, Barbara (2019)In: American Sociological Review 84(6), pp. 1134-1158.ABSTRACT“Corporations gather massive amounts of personal data to predict how individuals will behave so that they can profitably price goods and allocate resources. This article investigates the moral foundations of such increasingly prevalent market practices. I leverage the case of credit scores in car insurance pricing—an early and controversial use of algorithmic prediction in the U.S. consumer economy—to unpack the premise that predictive data are fair to use and to understand the conditions under which people are likely to challenge that moral logic. Policymaker resistance to credit-based insurance scores reveals that contention arises when predictions depend on mathematical distinctions that do not align with broader understandings of good and bad behavior, and when theories about why predictions work point to the market holding people accountable for actions that are not really their fault. Via a de-commensuration process, policymakers realign the market with their own notions of moral deservingness. This article thus demonstrates the importance of causal understanding and moral categorization for people accepting markets as fair. As data and analytics permeate markets of all sorts, as well as other domains of social life, these findings have implications for how social scientists understand the novel forms of stratification that result.”EXCTRACT“These new market arrangements embody particular moral understandings that can be used to justify the system as well as any inequality it produces. Key among these is the idea that algorithmic predictions help allocate economic resources in a manner that provides consumers with the right prices and opportunities (Mayer-Schönberger and Cukier 2013). Companies explain data mining and prediction as a way to customize what individuals get—a way to anticipate people’s preferences, needs, and wants, and present what is most relevant (Cohen 2013; Seaver 2015; Zwick and Knott 2009). Yet right means more than well-matched. The data fed into algorithms are tied to individuals and their “behavior,” a way of understanding action that implies deliberate decisions for which people can be legitimately held to account (Boltanski and Thévenot 2006). Fourcade and Healy (2017:24) argue that the mass use of personal data therefore inculcates an “economy of moral judgment” in which “outcomes are experienced as morally deserved positions based on prior good actions and good tastes.” An individual rightly reaps the rewards or suffers the ill effects of her data because it is her data. Moreover, the method of moving from data to decision—computation—“seems dispassionate, impartial and objective” (Fourcade and Healy 2017:17), bolstering the sense that predictions indicate not only what people will want, but also what they ought to get. […] In this article, I investigate the moral economy of algorithmic prediction by studying public policy debates. The value of studying such debates is that as social actors justify their actions and ideas and challenge those of others, they draw on taken-for-granted understandings, thus articulating assumptions and worldviews that otherwise might remain in the background (Boltanski and Thévenot 2006; Swidler 1986).” (p. 1137/1141)