Sample Project: Lie Detection
Lie detection algorithms attract few users but vastly increase accusation rates.
People are not very good at detecting lies, which may explain why they refrain from accusing others of lying, given the social costs attached to false accusations—both for the accuser and the accused. In the Algorithmic Lie Detection project, we consider how this social balance might be disrupted by the availability of lie-detection algorithms powered by Artificial Intelligence. Will people elect to use lie detection algorithms that perform better than humans and, if so, will they show less restraint in their accusations? We built a machine learning classifier whose accuracy (67%) was significantly better than human accuracy (50%) in a lie-detection task, and conducted an incentivized lie-detection experiment in which we measured participants’ propensity to use the algorithm, as well as the impact of that use on accusation rates (for an overview of the study design, see Figure 1).
Our results reveal that only a few people (33%) who elect to use the algorithm drastically increase their accusation rates (from 25% in the baseline condition up to 86% when the algorithm flags a statement as a lie). They make more false accusations (18pp increase), while the probability of a lie remaining undetected is much lower in this group (36pp decrease). We consider individual motivations for using lie detection algorithms and the social implications of these algorithms (see Figure 2 for results).
von Schenk, A., Klockmann, V., Bonnefon, J. F., Rahwan, I., & Köbis, N. (2022). Lie detection algorithms attract few users but vastly increase accusation rates. arXiv preprint arXiv:2212.04277.