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

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