Lie detection algorithms attract few users but vastly increase accusation rates
This research addresses the social implications of AI lie detection for human decision-making, showing incremental effects on accusation behavior.
The study investigated how AI-powered lie detection algorithms affect human behavior, finding that while only 33% of participants used the algorithm, those who did increased their accusation rates from 25% to 86% when the algorithm flagged lies, leading to an 18 percentage point rise in false accusations but a 36 percentage point reduction in undetected lies.
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. Here 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. We find that the 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), but at the same time, 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.