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.

How do humans calibrate and maintain trust in AI systems? Does polished AI output mislead users about actual accuracy and expertise? How should AI ethics integrate consciousness, values, and transparent disclosure? How can we determine if computational systems are genuinely conscious? How much do stated privacy concerns predict actual privacy-protective behavior? How can persona-based systems overcome their inherent limitations in modeling diversity? Why do users believe false claims despite fact-checking efforts? How should control and autonomy be allocated in human-AI collaboration? Do engagement-optimized chatbots create dependencies that undermine user autonomy? Why do consumers overtrust AI preference agents despite limited understanding? How can we distinguish genuine model self-knowledge from behavioral performance? What conversation mechanics enable genuine human-AI communicative participation?