TY  - JOUR
CY  - Cambridge
ID  - publications50334
UR  - http://tse-fr.eu/pub/130255
IS  - n°7
A1  - Köbis, Nils
A1  - von Schenk, Alicia
A1  - Klockmann, Victor
A1  - Bonnefon, Jean-François
A1  - Rahwan, Iyad
Y1  - 2024/06//
N2  - 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.
PB  - Elsevier
JF  - iScience
VL  - vol.27
SN  - 2589-0042
TI  - Lie detection algorithms disrupt the social dynamics of accusation behavior
AV  - none
ER  -