Product Design & UX · Chapter 2 of 5
Designing for systems that are sometimes wrong
Named in 93% of postings. The genuinely new design problem, and almost nobody's portfolio shows it.
Conventional interface design assumes determinism: the button does the thing. An AI feature produces a plausible answer that is sometimes wrong, not reproducibly, and the design has to carry that.
The five decisions that define an AI interface
- Disclosure. Does the user know they are interacting with a system? In many jurisdictions this is now a legal requirement, not a preference.
- Confidence. Do you show it? Numeric confidence is usually meaningless to users and often miscalibrated; hedged language and visible sources generally work better.
- Correction. How does someone fix a wrong answer, and does the system learn from it? A feature with no correction path teaches users to abandon it after the first error.
- The escape hatch. An obvious route to a human or to a manual path. Its usage rate is one of the best quality signals a team has.
- Failure presentation. A confidently wrong answer is far more damaging than a visible refusal. Designing the ‘I am not sure’ state well is most of the safety work.
The portfolio piece to build
Take any AI feature — a summariser, a search, an assistant — and design its wrong states. What it looks like when it is unsure, when it is wrong and the user notices, when it is wrong and they do not, and how they correct it.
Almost every AI portfolio piece currently shows the happy path. Showing the failure design is a genuine differentiator and it demonstrates the judgement the 93% figure is really asking for.