Is Your Machine Better Than You? You May Never Know

54 Pages Posted: 24 May 2022 Last revised: 9 Dec 2022

See all articles by Francis deVericourt

Francis deVericourt

ESMT European School of Management and Technology

Huseyin Gurkan

ESMT European School of Management and Technology

Date Written: December 8, 2022

Abstract

Artificial intelligence systems are increasingly demonstrating their capacity to make better predictions than human experts. Yet, recent studies suggest that professionals sometimes doubt the quality of these systems and overrule machine based prescriptions. This paper explores the extent to which a decision maker (DM) supervising a machine to make high-stake decisions can properly assess whether the machine produces better recommendations. To that end, we study a set-up in which a machine performs repeated decision tasks (e.g., whether to perform a biopsy) under the DM’s supervision. Because stakes are high, the DM primarily focuses on making the best choice for the task at hand. Nonetheless, as the DM observes the correctness of the machine’s prescriptions across tasks, she updates her belief about the machine. However, the DM is subject to a so-called verification bias such that the DM verifies the machine’s correctness and updates her belief accordingly only if she ultimately decides to act on the task. In this set-up, we characterize the evolution of the DM’s belief and overruling decisions over time. We identify situations under which the DM hesitates forever whether the machine is better, i.e., she never fully ignores but regularly overrules it. Moreover, the DM sometimes wrongly believes with positive probability that the machine is better. We fully characterize the conditions under which these learning failures occur and explore how mistrusting the machine affects them. These findings provide a novel explanation for human-machine complementarity and suggest guidelines on the decision to fully adopt or reject a machine.

Keywords: machine accuracy, decision making, human-in-the-loop, algorithm aversion, dynamic learning

Suggested Citation

deVericourt, Francis and Gurkan, Huseyin, Is Your Machine Better Than You? You May Never Know (December 8, 2022). ESMT Berlin Working Paper 22-02 (R1), Available at SSRN: https://ssrn.com/abstract=4117641 or http://dx.doi.org/10.2139/ssrn.4117641

Francis DeVericourt (Contact Author)

ESMT European School of Management and Technology ( email )

Schlossplatz 1
10117 Berlin
Germany

Huseyin Gurkan

ESMT European School of Management and Technology ( email )

Schlossplatz 1
10117 Berlin
Germany

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