  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.
