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      A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores

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          Abstract

          The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologies, it is key to study how experts alter their decisions when using such tools. In this paper, we study the adoption of an algorithmic tool used to assist child maltreatment hotline screening decisions. We focus on the question: Are humans capable of identifying cases in which the machine is wrong, and of overriding those recommendations? We first show that humans do alter their behavior when the tool is deployed. Then, we show that humans are less likely to adhere to the machine's recommendation when the score displayed is an incorrect estimate of risk, even when overriding the recommendation requires supervisory approval. These results highlight the risks of full automation and the importance of designing decision pipelines that provide humans with autonomy.

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          Author and article information

          Journal
          19 February 2020
          Article
          10.1145/3313831.3376638
          2002.08035
          aa1793f4-4a38-4a11-a358-3c40bc526403

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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          cs.CY

          Applied computer science
          Applied computer science

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