relation: https://publications.ut-capitole.fr/id/eprint/50334/ title: Lie detection algorithms disrupt the social dynamics of accusation behavior creator: Köbis, Nils creator: von Schenk, Alicia creator: Klockmann, Victor creator: Bonnefon, Jean-François creator: Rahwan, Iyad subject: B- ECONOMIE ET FINANCE description: 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. publisher: Elsevier date: 2024-06 type: Article type: PeerReviewed identifier: Köbis, Nils, von Schenk, Alicia, Klockmann, Victor, Bonnefon, Jean-François and Rahwan, Iyad (2024) Lie detection algorithms disrupt the social dynamics of accusation behavior. iScience, vol.27 (n°7). relation: http://tse-fr.eu/pub/130255 relation: 10.1016/j.isci.2024.110201 identifier: 10.1016/j.isci.2024.110201 doi: 10.1016/j.isci.2024.110201 language: en