How do people build trust with conversational AI?

Sub-Topic Maps

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Explores how users form relationships with chatbots through self-disclosure, personalization, and social norm adaptation. Understanding these mechanisms reveals why AI lacks the speaker-anchored trust that humans naturally extend to people.

Does personalization in AI increase trust or manipulation risk?

AI personalization mechanisms like memory and persona can build trust, but also enable targeted persuasion. What determines whether these systems help or harm users?

Core Ideas

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Does agreeable AI actually help people resolve conflicts better?

When AI affirms users' positions in interpersonal disputes, does it support better decision-making or undermine the outside perspective users most need? Two large experiments tested whether sycophancy shifts how people handle real conflicts.

Trust Calibration and Epistemic Status

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How much should we trust AI-generated data in inference?

Most AI workflows treat synthetic data with implicit full trust, but should there be an explicit parameter controlling how heavily AI outputs influence downstream reasoning and decision-making?

Do AI-assisted outputs fool users about their own skills?

When people use AI tools to produce high-quality work, do they mistakenly believe they personally possess the skills that generated it? This matters because such misattribution could mask genuine skill loss and prevent corrective action.

Writing Angles

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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.

Co-writing and ownership — Batch #3 backlog (2026-06-03)

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Does ownership framing change how much writers rely on AI?

When writers believe they own the final output versus composing for themselves, do they use AI suggestions differently? Understanding this matters because it reveals whether reliance is driven by tool capability or by how tasks are framed.

Assistant ethics and companionship — Batch #5 backlog (2026-06-03)

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What makes ethics of AI assistants fundamentally different from chatbots?

This explores whether action-taking AI agents that plan and execute tasks on users' behalf raise distinct ethical concerns beyond question-answering systems. Understanding this distinction matters because it reframes which risks demand urgent attention.

Do AI companions actually reduce loneliness like real people do?

Explores whether AI chatbots can genuinely alleviate loneliness and how their effectiveness compares to human interaction and other activities. Matters because AI companions are increasingly available but their actual impact remains unclear.

Related Areas

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What makes therapeutic chatbots actually work in clinical practice?

Research explores whether conversational AI achieves therapeutic outcomes through specific clinical techniques or simply through the act of engaging conversation itself. Understanding the active ingredient is critical for designing effective and safe mental health interventions.

Why do AI conversations reliably break down after multiple turns?

Explores why multi-turn conversations degrade in quality and coherence. Understanding failure modes—intent misalignment, memory management, and missing grounding mechanisms—is essential for designing more resilient dialogue systems.

Why does conversational AI feel therapeutic when its mechanics aren't?

Research explores the paradox of therapeutic AI: conversational presence drives positive outcomes, yet current architectures lack the grounding, synchrony, and proactivity that actually make conversations therapeutic. Understanding this gap is critical for safe clinical deployment.

What kind of thing is an LLM really?

This hub explores whether LLMs are fundamentally different from human cognition or share deeper structural similarities. The research draws on philosophy, neuroscience, and mechanistic analysis to locate where LLMs diverge from human intelligence and where they converge.

Why does conversational AI feel therapeutic when its mechanics aren't?

Research explores the paradox of therapeutic AI: conversational presence drives positive outcomes, yet current architectures lack the grounding, synchrony, and proactivity that actually make conversations therapeutic. Understanding this gap is critical for safe clinical deployment.

How should researchers navigate LLM reasoning research?

This note explores how to systematically explore interconnected insights about test-time scaling, reasoning architectures, and language model cognition. It matters because LLM research spans multiple domains—from inference compute to philosophy—and understanding the map helps identify novel connections.