Soul of a new machine: Self-learning algorithms in public administration

Abstract

Big data sets in conjunction with self-learning algorithms are becoming increasingly important in public administration. A growing body of literature demonstrates that the use of such technologies poses fundamental questions about the way in which predictions are generated, and the extent to which such predictions may be used in policy making. Complementing other recent works, the goal of this article is to open the machine’s black box to understand and critically examine how self-learning algorithms gain agency by transforming raw data into policy recommendations that are then used by policy makers. I identify five major concerns and discuss the implications for policy making.