Are Customers Lying to Your Chatbot?

Paper · Source
Social Theory and SocietyNatural Language Inference

Dishonesty is far from a new phenomenon. But as chatbots, online forms, and other digital interfaces grow more and more common across a wide range of customer service applications, bending the truth to save a buck has become easier than ever. How can companies encourage their customers to be honest while still reaping the benefits of automated tools?

In this experiment, we first assessed participants’ general tendency to cheat by asking them to flip a coin ten times and report the results via an online form, and then categorized them accordingly as “likely cheaters” and “likely truth-tellers.” In the next part of the experiment, we offered them the choice between reporting their coin flips to a human or via an online form. Overall, roughly half of the participants preferred a human and half preferred the online form — but when we took a closer look, we found that “likely cheaters” were significantly more likely to choose the online form, while “likely truth-tellers” preferred to report to a human. This suggests that people who are more likely to cheat proactively try to avoid situations in which they have to do so to a person (rather than to a machine), presumably due to a conscious or subconscious awareness that lying to a human would be more psychologically unpleasant.

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

Why do people disclose to AI systems despite their artificial nature? What drives appropriate trust calibration in personalized AI systems? Why does polished presentation create unearned authority in AI outputs? How well do AI systems understand human social norms? What design and behavioral factors drive false consciousness attribution to AI? How can we distinguish genuine model deception from honest errors? What safeguards enable trustworthy AI-assisted scientific peer review at scale? When should work require human-AI partnership versus full automation? How do neighboring agents influence whether others cooperate or collude? Should AI communication design follow human conversation norms or develop distinct machine-specific principles?