Designing AI Features Users Can Trust
Design AI experiences that set expectations, show sources and make mistakes easy to fix
A taste of a lesson
Should our contract review tool show a confidence percentage next to each flagged clause?
Only if the number is calibrated, meaning that clauses marked 80 percent are right about 80 percent of the time on real data. Most raw model scores are not, and a precise looking number invites over trust. Often better: show the exact text that triggered the flag and the rule it relates to, so the reviewer can judge quickly, and perhaps a simple label like 'needs a closer look' when your evaluation supports it. How would your users check a flag today, and how long does that take them?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Explain calibrated trust and why both over trust and under trust hurt users
- Design clear expectation setting without long disclaimers
- Show sources and inputs so users can verify AI output
- Design control, error, refusal and feedback states
- Review screens with a practical AI trust checklist
Lesson plan
- 1 What calibrated trust means Understand why the aim is appropriate trust, not maximum trust. Start
- 2 Setting expectations at the point of use Tell users what the feature does and cannot do, briefly and in context. Start
- 3 Sources, evidence and uncertainty Help users check output by showing where it came from. Start
- 4 Control, review and undo Keep users in charge of what the AI changes. Start
- 5 Errors, refusals and feedback Design the moments when the AI fails so users can recover. Start
- 6 Patterns to avoid and a trust checklist Spot misleading patterns and review designs with a checklist. Start
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About this tutor
For product managers, designers, founders and anyone shaping how users meet an AI feature. Beginners learn the core principles; experienced designers sharpen them on real patterns. You will study how to set honest expectations, show where answers come from, express uncertainty without drowning users in warnings, keep users in control with review and undo, design for errors and refusals, handle feedback, and avoid patterns that build false trust such as overconfident language or human like personas that mislead. You review screens from your own product or worked examples and finish with a trust checklist you can use in design reviews.
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About the teacher
Product management for AI features: deciding, specifying, testing and pricing them well
9 tutors 388 lessons taught Sample
I teach product managers and founders how to build AI features that people trust and keep using. I come from product work on software teams, where I learned that the hard part of an AI feature is rarely the model. It is deciding whether the feature should exist, writing down what good looks like, testing it before customers do and...
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