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Check the box! How to deal with automation bias in AI-based personnel selection

Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system’s recommendation. As this...

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Autores principales: Kupfer, Cordula, Prassl, Rita, Fleiß, Jürgen, Malin, Christine, Thalmann, Stefan, Kubicek, Bettina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113449/
https://www.ncbi.nlm.nih.gov/pubmed/37089740
http://dx.doi.org/10.3389/fpsyg.2023.1118723
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author Kupfer, Cordula
Prassl, Rita
Fleiß, Jürgen
Malin, Christine
Thalmann, Stefan
Kubicek, Bettina
author_facet Kupfer, Cordula
Prassl, Rita
Fleiß, Jürgen
Malin, Christine
Thalmann, Stefan
Kubicek, Bettina
author_sort Kupfer, Cordula
collection PubMed
description Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system’s recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.
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spelling pubmed-101134492023-04-20 Check the box! How to deal with automation bias in AI-based personnel selection Kupfer, Cordula Prassl, Rita Fleiß, Jürgen Malin, Christine Thalmann, Stefan Kubicek, Bettina Front Psychol Psychology Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system’s recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality. Frontiers Media S.A. 2023-04-05 /pmc/articles/PMC10113449/ /pubmed/37089740 http://dx.doi.org/10.3389/fpsyg.2023.1118723 Text en Copyright © 2023 Kupfer, Prassl, Fleiß, Malin, Thalmann and Kubicek. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Kupfer, Cordula
Prassl, Rita
Fleiß, Jürgen
Malin, Christine
Thalmann, Stefan
Kubicek, Bettina
Check the box! How to deal with automation bias in AI-based personnel selection
title Check the box! How to deal with automation bias in AI-based personnel selection
title_full Check the box! How to deal with automation bias in AI-based personnel selection
title_fullStr Check the box! How to deal with automation bias in AI-based personnel selection
title_full_unstemmed Check the box! How to deal with automation bias in AI-based personnel selection
title_short Check the box! How to deal with automation bias in AI-based personnel selection
title_sort check the box! how to deal with automation bias in ai-based personnel selection
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113449/
https://www.ncbi.nlm.nih.gov/pubmed/37089740
http://dx.doi.org/10.3389/fpsyg.2023.1118723
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