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Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making

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  • Daniela Sele
  • Marina Chugunova

Abstract

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions.

Suggested Citation

  • Daniela Sele & Marina Chugunova, 2024. "Putting a human in the loop: Increasing uptake, but decreasing accuracy of automated decision-making," PLOS ONE, Public Library of Science, vol. 19(2), pages 1-14, February.
    • Handle: RePEc:plo:pone00:0298037
    DOI: 10.1371/journal.pone.0298037
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    1. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    2. Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawArchive 97upg_v1, Center for Open Science.
    3. Edwards, Lilian & Veale, Michael, 2017. "Slave to the Algorithm? Why a 'right to an explanation' is probably not the remedy you are looking for," LawRxiv 97upg, Center for Open Science.
    4. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
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