Judgment Time: Normative Dimensions of Algorithmic Speed

Daniel Susser (Pennsylvania State University)

Abstract: Existing discussions about automated decision-making focus primarily on its inputs and outputs, raising questions about data collection and privacy on the one hand and accuracy and fairness on the other. Less attention has been devoted to critically examining the temporality of decision-making processes—the speed at which automated decisions are reached. In this paper, I identify four dimensions of algorithmic speed that merit closer analysis. Duration (how much time it takes to reach a judgment), timing (when automated systems intervene in the activity being evaluated), frequency (how often evaluations are performed), and lived time (the human experience of algorithmic speed) are interrelated, but distinct, features of automated decision-making. And choices about the temporal structure of automated decision-making systems have normative implications—accuracy, fairness, legitimacy, and other values hang in the balance. As computational tools are increasingly tasked with making judgments about human activities and practices, the designers of decision-making systems will have to reckon, I argue, with when—and how fast—judgments ought to be rendered. Though computers are capable of reaching decisions at incredible speeds, failing to account for the temporality of automated decision-making risks misapprehending the costs and benefits automation promises.

Bio: A philosopher by training, Daniel works at the intersection of technology, ethics, and policy. His research aims to highlight normative issues in the design, development, and use of digital technologies, and to clarify conceptual issues that stand in the way of addressing them through law and policy. At the moment, he’s especially focused on questions about privacy, online influence, and automated decision-making.