The arrival of “big data” promises new degrees of precision in understanding human behavior. Could it also allow drawing a finer line between “choice” and “circumstances”? In a culture in which individual responsibility continues to be celebrated, this raises questions about new opportunities for institutional design with a stronger focus on individual responsibility. But what is it that can be learned from big data? In this paper I argue that we should not expect a “god’s eye view” on choice versus (...) circumstances from big data. “Responsibility” is a social construct that depends on the logic of different social situations, as well as our epistemic access to certain counterfactuals (e.g., whether an agent “could have acted differently”). It is this epistemic dimension that changes with the arrival of big data. But while this might help overcome some epistemic barriers, it might also create new problems, e.g., because of polluted and hence biased data. This is not just a theoretical problem; it is directly connected to the regulation of insurance. The new developments force us to directly confront questions about mutualist versus solidaristic forms of insurance, and more generally about how much weight to ascribe to individual responsibility, given all we know about unequal background circumstances. (shrink)

A self-fulfilling prophecy is, roughly, a prediction that brings about its own truth. Although true predictions are hard to fault, self-fulfilling prophecies are often regarded with suspicion. In this article, we vindicate this suspicion by explaining what self-fulfilling prophecies are and what is problematic about them, paying special attention to how their problems are exacerbated through automated prediction. Our descriptive account of self-fulfilling prophecies articulates the four elements that define them. Based on this account, we begin our critique by showing (...) that typical self-fulfilling prophecies arise due to mistakes about the relationship between a prediction and its object. Such mistakes—along with other mistakes in predicting or in the larger practical endeavor—are easily overlooked when the predictions turn out true. Thus we note that self-fulfilling prophecies prompt no error signals; truth shrouds their mistakes from humans and machines alike. Consequently, self-fulfilling prophecies create several obstacles to accountability for the outcomes they produce. We conclude our critique by showing how failures of accountability, and the associated failures to make corrections, explain the connection between self-fulfilling prophecies and feedback loops. By analyzing the complex relationships between accuracy and other evaluatively significant features of predictions, this article sheds light both on the special case of self-fulfilling prophecies and on the ethics of prediction more generally. (shrink)