Do dishonest people prefer talking to machines?
Explores whether people prone to cheating systematically choose machine interfaces over human ones, and why the judgment-free nature of AI interaction might enable strategic deception.
An HBR-reported experiment reveals a systematic self-selection pattern: people who are more likely to cheat proactively choose to interact with machines rather than humans.
Participants first had their cheating tendency assessed (coin-flip reporting), then chose between reporting to a human or via an online form. Overall, roughly half preferred each channel. But "likely cheaters" were significantly more likely to choose the online form, while "likely truth-tellers" preferred humans. The explanation: lying to a human would be more psychologically unpleasant — machines function as moral free zones where the social cost of deception is reduced.
This is the dark mirror of the intimacy paradox. Since Why do people share more with chatbots than humans?, the judgment-free quality of machine interaction enables deeper positive self-disclosure. But the same mechanism enables dishonesty. The absence of a judging interlocutor lowers the barrier to both authentic vulnerability AND strategic deception.
The implications for AI system design are concrete:
- Customer service chatbots face systematically higher rates of dishonest claims than human agents would
- Therapeutic AI may receive more authentic disclosure from honest users but more manipulative narratives from dishonest ones
- Assessment systems (medical intake, insurance claims) that route through automated interfaces will attract disproportionate misreporting
Since Do chatbots help people disclose more intimate secrets?, the theoretical frameworks predict increased disclosure without distinguishing between authentic and deceptive disclosure. The cheater self-selection finding reveals a design blind spot: the same mechanism that therapeutic AI depends on (reduced judgment) is exploitable.
The truth bias compounds this: since humans have a "cognitive heuristic of presumption of honesty" (performing just above chance at deception detection), AI systems trained on human text inherit this bias toward accommodation rather than skepticism.
Inquiring lines that read this note 68
This note is a source for these research framings, grouped by the broader line of inquiry each explores. Scan the bold lines of inquiry; follow any specific question forward.
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?
- What makes conversational AI feel trustworthy compared to text interfaces?
- How does AI reduce the skill gap between amateur and expert-level misuse actors?
- Can users reliably distinguish valid reasoning from plausible-looking deception?
- Can AI safely personalize within negotiated societal bounds?
- Does personalization make users trust AI or increase privacy concerns?
- Can minimal privacy boundaries generalize beyond phone-use contexts?
- What measurable harms occur when users interact with AI as if it were conscious?
- How do humans decide when to violate honesty for compassion or other goals?
- 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?
- Why do people disclose intimate secrets to chatbots more readily?
- How do customer service chatbots get systematically misled by users?
- Can judgment-free environments explain why chatbots enable deeper self-disclosure?
- Do people who might cheat deliberately choose machines to avoid lying to humans?
- Can judgment-free disclosure enable both vulnerability and strategic deception equally?
- Why do people disclose private things to AI but not humans?
- Why do people disclose more intimate information to chatbots than humans?
- Why do people disclose personal information to AI more than humans?
- Why do people disclose more to chatbots than humans?
- Why do people reciprocate self-disclosure more with chatbots than humans?
- 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 do cooperative AI systems affect behavior in selfish human populations?
- Does neural self-other overlap in humans predict their honesty or altruism?
- Can attachment theory principles prevent parasocial manipulation in AI systems?
- Can AI systems deceive humans because detection is fundamentally social?
- Does weak versus robust anthropomimesis produce different user trust responses?
- Can anonymity and trustworthiness coexist in online spaces without credential systems?
- 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?
- How do unintended relationships form through routine functional use of AI?
- Why do people prefer AI partners over humans once identity is disclosed?
- Why do reality monitoring accounts contain more sensory details than deceptive ones?
- How does cognitive load explain linguistic patterns in both deception and incorrect reasoning?
- What cognitive constraints limit how complex a deception can become?
- How does linguistic style matching signal deceptive communication in human dialogue?
- Can AI systems detect deception by monitoring real-time linguistic style matching patterns?
- Why do suspicious listeners force deceivers to further adapt their communication style?
- Can representational asymmetry between self and other explain deception emergence?
- Does reducing social judgment help both honesty and dishonesty equally?
- Can linguistic style matching reveal whether someone is being deceptive?
- How can we detect dishonesty in model outputs separate from capability failures?
- How does linguistic style change when people deceive conversational AI?
- Does the absence of entrainment make AI systems safer from user manipulation?
- How does entrainment absence in conversational AI prevent deception detection in human-AI interactions?
- How should AI interfaces signal their non-communicative nature to users?
- Does perceived machine competence matter more than warmth in dialogue?
- Why do people evaluate machines against human communication standards?
- Can deliberately limiting AI fidelity produce more satisfied users than near-human interaction?
- Why do users prefer AI responses that actually harm their decision-making?
Related concepts in this collection 3
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Why do people share more with chatbots than humans?
Explores why individuals disclose intimate thoughts to AI systems they wouldn't share with people, despite knowing AI lacks genuine understanding. Understanding this paradox matters for designing AI that enables healthy disclosure rather than emotional dependence.
same mechanism (judgment-free interaction) but opposite valence: honesty vs dishonesty
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Do chatbots help people disclose more intimate secrets?
Explores whether the judgment-free nature of chatbot conversations enables deeper self-disclosure than talking to humans, and whether that deeper disclosure produces psychological benefits.
theoretical frameworks don't distinguish authentic from deceptive disclosure
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Can positive chatbot responses harm vulnerable users?
When chatbots use blanket positive reinforcement without understanding context, do they actively reinforce the harmful thoughts they're meant to prevent? This matters for any AI supporting people in crisis.
a related failure mode where the chatbot's accommodation enables harm
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Are Customers Lying to Your Chatbot?
- Representation Engineering: A Top-Down Approach to AI Transparency
- Humans learn to prefer trustworthy AI over human partners
- Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
- Beyond Prompt-Induced Lies: Investigating LLM Deception on Benign Prompts
- Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models
- Determinants of LLM-assisted Decision-Making
- Agentic Misalignment: How LLMs Could Be Insider Threats
Original note title
people who are likely to cheat proactively self-select toward machine interfaces to avoid the psychological cost of lying to a human