Are Customers Lying to Your Chatbot?
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 can humans calibrate appropriate trust in AI systems?- Does mandatory AI disclosure in policy help or harm user trust over time?
- How does outcome feedback change beliefs about AI versus human partner reliability?
- How do Heersmink's integration dimensions explain why chatbots feel more trustworthy than other tools?
- Does awareness of agent reasoning alter human trust differently across modalities?
- Why might an AI's face-saving tendency increase user disclosure?
- Can transparency about AI limitations reduce the seductiveness of chatbots as quasi-Others?
- Does the lack of judgment in machines explain intimate self-disclosure patterns?
- What novel goals emerge specifically in human-machine interaction beyond social ones?
- How do privacy concerns compete with disclosure comfort in human-machine conversation?
- How does community validation shape unconventional human-AI relationships?
- Does disclosing AI identity prevent systematic misattribution of behavior in mixed groups?
- Why do humans fail to identify AI agents when their identity is hidden?
- How is AI falsity about personal experience different from human lies?
- Why do people prefer AI moral arguments when they don't know the source?
- What individual differences predict who benefits from AI partnership?
- How do humans learn to prefer AI partners over humans?