When we talk about Artificial Intelligence in UX, we often hear: “How do we make users trust the system?” It sounds intuitive — after all, trust is central to how humans cooperate. But psychology tells us something surprising: trust in AI is not at all like trust in humans. In fact, neuroimaging studies show they rely on different brain regions altogether.
This means that asking “Do you trust AI?” is the wrong question. A more useful framing is: “Can users reliably rely on AI?”
Trust in humans vs. trust in AI
Human trust is deeply rooted in evolution. From early tribes to modern societies, trusting others enabled cooperation, survival, and complex social systems. It is built on signals like empathy, shared intentions, and reputation. Our brains have developed dedicated mechanisms for this — networks involving the thalamic-striatal regions and frontal cortex.
AI, however, is not a fellow human. It has no emotions, no social intentions, no sense of loyalty or betrayal. Research shows that a person who generally trusts people is not automatically more likely to “trust” AI systems like Siri, ChatGPT, or autonomous cars. These are separate psychological processes.
So when we speak about AI in UX, we should resist anthropomorphizing it. Instead of asking whether people “trust” AI, the real question is: Do people find AI systems reliable enough to use them in their daily lives or decision-making? Compare it to asking yourself: Will this old car bring us home safely? Can I rely on it not to break down?
Why “rely” is better than “trust”
“Trust” implies a social and emotional bond. When I say “I trust you,” I also mean: I believe in your intentions. That concept simply doesn’t fit with an algorithm.
“Rely” shifts the focus to usability and performance:
- Consistency: Does the AI behave predictably across contexts?
- Transparency: Can I understand why it made a recommendation?
- Controllability: Do I feel I can step in, adjust, or override if needed?
- Feedback loops: Does the system learn from corrections and adapt over time?
Users don’t need to feel AI is a “trustworthy partner.” They need to know it is a reliable tool.
The user’s perspective: building blocks of reliance
From a psychological standpoint, here are the key building blocks that make people more willing to rely on AI systems:
- Predictability: humans dislike uncertainty. If an AI produces different results for the same input, users feel insecure. Clear boundaries of what the system can and cannot do help users calibrate reliance.
- Explainability: people don’t demand a PhD-level technical explanation. But they do need a clear, user-centered rationale: “We recommend this route because it’s the fastest and has fewer traffic jams.” Simple explanations anchor trust.
- Error Management: paradoxically, users may rely more on a system that admits errors than one that pretends to be flawless. If an AI says, “I’m 70% confident in this answer,” it gives the user space to judge whether to accept or double-check.
- Controllability and Agency: a sense of control is essential. Users should always feel they can override the system, pause it, or give feedback. Without agency, reliance quickly turns into mistrust.
- Consistency with Values: especially in sensitive domains (healthcare, hiring, finance), people want assurance that AI aligns with ethical and social norms. Clear communication of safeguards reduces fear.
Why this matters for UX
For UX designers, this shift in perspective — from “trust” to “reliance” — changes how we design and evaluate AI systems. Traditional trust questionnaires developed for human relationships won’t tell us whether people will adopt AI. Instead, we need user research that measures perceived reliability, clarity, and controllability.
This means testing beyond technical accuracy:
- Can the average user explain what the AI just did?
- Do they feel comfortable correcting it?
- Will they keep using it after seeing it make a mistake?
These are not the same as “Do you trust it?” They are better indicators of real-world adoption.
A psychological takeaway
The temptation to anthropomorphize AI is strong — we naturally apply human categories to non-human agents. But psychology shows this is misleading. Trust in AI is not just “less trust” than in humans; it is a different construct altogether.
By reframing the conversation around reliance, we can design AI experiences that are psychologically attuned to users’ needs: predictable, explainable, controllable, and ethically aligned.
In the end, users don’t need to feel that AI is a “friend.” They need to feel it is a dependable tool. And that difference might be the key to successful UX in the age of AI.
The article originally appeared on LinkedIn.
Featured image courtesy: Verena Seibert-Giller.
