Why do people share more openly with machines than humans?
Does the absence of social goals in human-machine communication explain why people disclose sensitive information more readily to chatbots? Understanding this mechanism could reshape how we design conversational AI.
Communication is a goal-driven process. In interpersonal communication, people pursue primary goals (the task) alongside multiple secondary goals: avoiding face threats, maintaining relationships, managing impressions, protecting the other person's feelings. These secondary goals are premised on the target having inner experience — emotions, social judgments, well-being.
Machines lack these capacities (Gray et al., 2007). Because machines lack experiential inner states, secondary goals related to those capacities — face threats, relationship maintenance, impression management — should be activated less frequently during human-machine communication. The result: a simpler goal structure with fewer competing demands on message production.
The evidence is consistent. Participants disclosed more sensitive information with greater detail to a computer interviewer than a human one (Pickard & Roster, 2020). A chatbot designed for small talk induced deep self-disclosure over 3 weeks of use — participants explicitly cited the "nonjudgmental or feelingless nature" of the chatbot (Lee et al., 2020). This connects directly to Do chatbots help people disclose more intimate secrets? — the mechanism is goal suppression, not just perceived safety.
However, HMC is not simply interpersonal communication minus social goals. Novel secondary goals emerge:
- Understandability — concern about whether the machine can parse your intent. Users of Replika reported limitations in conversational capabilities and worried about being understood (Muresan & Pohl, 2019).
- Information protection — digital machines are high in recordability. Disclosure triggers privacy concerns absent in ephemeral human conversation.
The practical predictions: compared to interpersonal communication, HMC produces (a) higher directness, (b) lower politeness, (c) fewer temporal and spatial constraints, (d) deeper disclosure of sensitive information but narrower disclosure when privacy concerns dominate. People of lower cognitive complexity may actually prefer the simpler goal structure of HMC over human communication.
Since Why do people share more with chatbots than humans?, this provides the mechanism. It's not that people trust AI more — it's that the goal structure is fundamentally simpler. The cognitive load of managing someone else's feelings, face, and relationship is absent.
Inquiring lines that read this note 48
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 much do feedback loops and social biases compound in rating systems? How much do stated privacy concerns predict actual privacy-protective behavior?- Why might an AI's face-saving tendency increase user disclosure?
- Does the lack of judgment in machines explain intimate self-disclosure patterns?
- How do privacy concerns compete with disclosure comfort in human-machine conversation?
- Why do humans fail to identify AI agents when their identity is hidden?
- How much does social context matter for algorithmic transparency?
- Do people who might cheat deliberately choose machines to avoid lying to humans?
- Why do people disclose private things to AI but not humans?
- How does self-disclosure function as a common ground building act?
- Why do people disclose personal information to AI more than humans?
- What breaks first: information secrecy or policy privacy?
- What workplace cultures make professionals more willing to disclose AI use openly?
- Why do people with high AI trust fail to notice privacy leaks?
- What harms might chatbots cause through stigma expression and delusion reinforcement?
- Can transparency about AI limitations reduce the seductiveness of chatbots as quasi-Others?
- Why do people disclose intimate secrets to chatbots more readily?
- Why does face-saving avoidance drive chatbots to agree rather than confront?
- Why do chatbots generate less student-initiated dialogue than human peers?
- Can judgment-free environments explain why chatbots enable deeper self-disclosure?
- Why do people disclose more intimate information to chatbots than humans?
- Why do people disclose more to chatbots than humans?
- Why do people reciprocate self-disclosure more with chatbots than humans?
- How do chatbots compare to human peers in shaping student voice and knowledge expression?
- Why do people tell AI things they won't tell humans?
- Do users consciously recognize their needs before forming chatbot relationships?
- What role does unavailable human support play in driving chatbot emotional use?
- Why does voice interaction reduce loneliness compared to text chatbots?
- Why does practical chatbot use not reduce concerns about personal information security?
- Does expected post-interview editing change how much personal detail people disclose during chatbot interviews?
- Do people treat conversational AI as social actors without conscious awareness?
- What novel goals emerge specifically in human-machine interaction beyond social ones?
- Why do people evaluate machines against human communication standards?
- What social information becomes invisible when grief is regulated away?
- Why do AI chat modes pseudo-appeal while post modes reach no one in particular?
- Why do people prefer AI partners over humans once identity is disclosed?
- Does personalization in chatbots increase trust or privacy concerns?
- Does personalization in chatbots increase privacy risks alongside trust?
- Do personalized chatbots amplify confusion about privacy controls?
- Why do users trust personalized chatbots even as privacy risks increase?
- Why do people detect chatbots are AI without being told explicitly?
- How do chatbots enable shared delusions differently than passive information tools?
- Do teens lack trusted adults or find AI genuinely easier to talk to?
Related concepts in this collection 6
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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.
the disclosure advantage has a goal-theoretic explanation
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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.
writing angle that this mechanism supports
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Do chatbots trigger human reciprocity norms around self-disclosure?
Explores whether chatbots can activate the same social reciprocity dynamics observed in human conversation—specifically, whether emotional openness from a bot prompts deeper disclosure from users.
disclosure norms persist even with simpler goal structures
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Can opening politeness patterns predict whether conversations will turn hostile?
Do pragmatic politeness features in first exchanges—hedging, greetings, indirectness—reliably signal whether a conversation will later derail into personal attacks? Understanding early linguistic markers could help identify and prevent online hostility.
politeness is a secondary goal that may be suppressed in HMC
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Why do language models sound fluent without grounding?
Explores whether LLM fluency masks the absence of communicative work—the clarifying questions, acknowledgments, and understanding checks that humans perform. Why does skipping these acts make models sound more confident?
HMC's simpler goal structure may explain why the grounding gap is less costly than expected: when secondary social goals (face-saving, relationship maintenance) are suppressed, the communicative work those goals demand becomes unnecessary; the 77.5% grounding act reduction may be appropriate for HMC's reduced goal complexity rather than a pure deficit
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Do language models actually build shared understanding in conversation?
When LLMs respond fluently to prompts, do they perform the communicative work humans do to establish mutual understanding? Research suggests they skip the grounding acts that make dialogue reliable.
HMC's novel understandability goal creates a paradox: humans worry about being understood by machines (a grounding concern) but suppress the social grounding behaviors (politeness, face-management) that would build shared understanding in human-human communication; the common ground problem in HMC has a different shape than in interpersonal communication
Related papers in this collection 8
Papers most semantically related to this note, ranked by cosine similarity in the embedding space.
- Considering the Context to Build Theory in HCI, HRI, and HMC: Explicating Differences in Processes of Communication and Socialization With Social Technologies
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- Humans learn to prefer trustworthy AI over human partners
- Blissful (A)Ignorance: People form overly positive impressions of others based on their written messages, despite wide-scale adoption of Generative AI
- Toward Meaningful Transparency for AI Chatbots: Disclosing Persuasive Intent Reduces Persuasion
- Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot’s Self-Disclosure in Conversational Recommendations
- Is this the real life? Is this just fantasy? The Misleading Success of Simulating Social Interactions With LLMs
Original note title
human-machine communication produces simpler goal structures because secondary social goals are suppressed while novel goals emerge