标题： 测谎算法吸引的用户很少，但大大提高了指控率 显示英文标题

标题： Lie detection algorithms attract few users but vastly increase accusation rates

摘要： 人们不太擅长识别谎言，这可能解释了为什么他们避免指责他人说谎，因为错误指控附带社会成本——对指控者和被指控者都有影响。 我们考虑了由人工智能驱动的谎言检测算法的可用性如何可能破坏这种社会平衡。 人们是否会选择使用比人类表现更好的谎言检测算法，如果会的话，他们在指控上是否会表现出更少的克制？ 我们构建了一个机器学习分类器，在谎言检测任务中的准确率（67%）显著高于人类的准确率（50%），并进行了一项有激励的谎言检测实验，测量参与者使用该算法的倾向，以及使用该算法对指控率的影响。 我们发现，仅少数人（33%）选择使用该算法，他们的指控率大幅增加（从基准条件下的25%上升到算法将某句话标记为谎言时的86%）。 他们做出更多的错误指控（增加了18个百分点），但与此同时，该群体中谎言未被检测到的概率要低得多（减少了36个百分点）。 我们考虑了人们使用谎言检测算法的个人动机以及这些算法的社会影响。 显示英文摘要

摘要： 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.