@inproceedings{cf661d7dc970407a9cb1b7d64840296a,
title = "A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores",
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.",
keywords = "algorithm assisted decision making, algorithm aversion, automation bias, child welfare, decision support, human-in-the-loop",
author = "Maria De-Arteaga and Riccardo Fogliato and Alexandra Chouldechova",
note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 2020 ACM CHI Conference on Human Factors in Computing Systems, CHI 2020 ; Conference date: 25-04-2020 Through 30-04-2020",
year = "2020",
month = apr,
day = "21",
doi = "10.1145/3313831.3376638",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "1--12",
booktitle = "CHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems",
address = "United States",
}