Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making

Daniela Sele and Marina Chugunova
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Daniela Sele: ETH
Marina Chugunova: Max Planck Institute for Innovation and Competition

No 438, Rationality and Competition Discussion Paper Series from CRC TRR 190 Rationality and Competition

Abstract: Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing availability of algorithmic recommendations and current intense discussions about regulation of automated decision-making. In an online experiment, we find that 66% of participants prefer algorithmic to equally accurate human recommendations if the decision is delegated fully. This preference for algorithms increases by further 7 percentage points if participants are able to monitor and adjust the recommendations before the decision is made. In line with automation bias, participants adjust the recommendations that stem from an algorithm by less than those from another human. Importantly, participants are less likely to intervene with the least accurate recommendations and adjust them by less, raising concerns about the monitoring ability of a human in a Human-in-the-Loop system. Our results document a trade-off: while allowing people to adjust algorithmic recommendations increases their uptake, the adjustments made by the human monitors reduce the quality of final decisions.

Keywords: automated decision-making; algorithm aversion; algorithm appreciation; automation bias (search for similar items in EconPapers)
JEL-codes: C90 D90 O33 (search for similar items in EconPapers)
Date: 2023-10-24
New Economics Papers: this item is included in nep-ain, nep-cmp and nep-exp
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