A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Real Judgments

Abstract

We propose a simple explanation to reconcile research documenting algorithm aversion with research documenting algorithm appreciation: elicitation methods. We compare self-reports and judgments. When making judgments, people consistently utilize algorithmic advice more than human advice. In contrast, hypotheticals produce unstable preferences; people sometimes report indifference and sometimes report preferring human judgment. Moreover, people fail to correctly anticipate behavior, utilizing algorithmic advice more than they anticipate. A slight change in the framing of a hypothetical task additionally moderates algorithm aversion. Stated preferences about algorithms are less stable than judgments, suggesting that algorithm aversion may be less stable than previous research leads us to believe. Importantly, focusing on hypothetical results has already led researchers and managers to overlook the important question of how people utilize and interact with algorithms.

Keywords: Algorithms, Big Data, Judgment and Decision Making, Future of Work, Psychology of Technology, Theory of Machine