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Calibrated Trust

Show users when to trust, verify, and override AI output.

How to apply it

Always: cite sources, expose confidence honestly, give a quick override path.

Calibrated Trust in practice: a concrete example

An AI assistant that shows its receipts

When Perplexity answers a question it puts a numbered citation on every claim, so you can click through and check the one line you doubt instead of trusting or dismissing the whole answer. Compare a chatbot that states a wrong flight time in the same confident tone it uses for everything else. The first earns calibrated trust: you lean on it where it is strong and verify where it matters. The second teaches you to either over-trust it and get burned, or ignore it entirely.

Common Calibrated Trust mistakes

  • Presenting every output in the same confident tone, so users can't tell a solid answer from a guess.
  • Hiding sources and reasoning, which forces an all-or-nothing choice: trust blindly, or don't trust at all.
  • Making the override path slow or buried, so when the AI is wrong the user can't easily take back control.

When Calibrated Trust doesn't apply

For low-stakes, easily reversible output (a draft caption, a first-pass colour suggestion) heavy confidence UI and citations are just noise. Reserve the full trust scaffolding for decisions that cost money, touch real data, or are hard to undo, where a wrong answer accepted silently does real harm.

Practise it on a real challenge

AI Summaries — the ones that quietly hallucinate

An AI feature generates summaries, and users occasionally get a confident, wrong one with no way to tell — design the loop that catches it before they trust it.

AI Travel Assistant — Wrong Train Booking

An assistant booked a non-refundable 06:12 train after an ambiguous request for a 'flexible morning trip'.

Health Assistant — Unsafe Advice

A health assistant gives overly confident advice without explaining limits or directing the user to professional help.

Recruitment Copilot — Biased Shortlist

An AI tool silently filters candidates using unclear criteria.

Smart Calendar — Unexpected Auto-Reschedule

A calendar agent moves an important meeting based on incomplete context.

Email Assistant — One-Tap Reply, Wrong Tone

A one-tap smart reply sends instantly, in a tone that doesn't fit the thread.

Chat App — Your Messages Train the Model by Default

A new AI feature is on by default and trains on private messages; opt-out is buried.

Research Assistant — A Confident, Fake Citation

An AI answer includes a specific-looking source that doesn't exist.

Support Bot — No Way to Reach a Human

A support bot loops the user through canned answers with no path to a human.

Marketplace — AI Quietly Sets a Different Price for You

An AI raises a user's price based on their behaviour, with no disclosure.

Coding Assistant — Auto-Accepts Its Own Suggestions

A coding assistant auto-applies its completions, so the developer stops reviewing what lands in the file.

Photo App — AI 'Cleanup' Deletes the Wrong Shots

An AI 'cleanup' permanently removes photos it judges duplicate or blurry — some the user wanted.

Content Moderation — Removed With No Reason

An AI removes a user's post and gives no specific reason and no way to appeal.

More UX laws

  • 60-30-10 Rule
  • 8-pt Spacing Scale
  • Accessible contrast
  • Aesthetic-Usability Effect
  • Alignment Principle
  • Density Matches Context
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