Köbis, Nils, von Schenk, Alicia, Klockmann, Victor, Bonnefon, Jean-FrançoisIdRef and Rahwan, IyadIdRef (2024) Lie detection algorithms disrupt the social dynamics of accusation behavior. iScience, Vol. 27 (N° 7).

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Identification Number : 10.1016/j.isci.2024.110201

Abstract

Humans, aware of the social costs associated with false accusations, are generally hesitant to accuse others of lying. Our study shows how lie detection algorithms disrupt this social dynamic. We develop a supervised machine-learning classifier that surpasses human accuracy and conduct a large-scale incentivized experiment manipulating the availability of this lie-detection algorithm. In the absence of algorithmic support, people are reluctant to accuse others of lying, but when the algorithm becomes available, a minority actively seeks its prediction and consistently relies on it for accusations. Although those who request machine predictions are not inherently more prone to accuse, they more willingly follow predictions that suggest accusation than those who receive such predictions without actively seeking them.

Item Type: Article
Language: English
Date: 19 June 2024
Refereed: Yes
Place of Publication: Cambridge
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 05 Feb 2025 15:26
Last Modified: 24 Apr 2025 07:50
OAI Identifier: oai:tse-fr.eu:130255
URI: https://publications.ut-capitole.fr/id/eprint/50334
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