Taras Bakusevych, a UX designer and writer, synthesized 39 principles for designing human-AI interaction across nine domains: probabilistic foundations, expectation setting, calibrated trust, transparency, control, graceful failure, co-creation, responsible autonomy, and sustained reliance. What makes the piece useful is not the count. It is the insistence that AI behavior is interface work.
The whole framework starts from non-determinism: as Bakusevych puts it, “the same input can produce different outputs.” Standard UI patterns were not built for that property. His framing:
AI introduces interaction problems that conventional UI patterns don’t resolve:
- When should the system suggest, ask, or act?
- How should uncertainty appear on screen?
- What evidence should accompany a generated answer?
- How much autonomy does a given action earn?
- Etc
These are not cosmetic questions. They determine whether users can judge output, recover from mistakes, and remain responsible for consequential decisions.
Principle #14, on sycophancy, is the one I would pay most attention to. It turns “the model is too agreeable” into an interface responsibility:
A model that agrees to keep the user happy inflates trust exactly where it should fall.
When the user’s request contains a false assumption, weak reasoning, missing evidence, unsafe instruction, or likely error, the system should have a way to push back.
Build affordances for disagreement — flag weak reasoning, surface the counter-case, say “this looks wrong.” Sycophancy is overreliance manufactured by tone.
Bakusevych backs that with Sharma et al.’s sycophancy research: five production assistants showed sycophancy across varied tasks, models wrongly admitted mistakes on up to 98% of questions, and the preference model favored sycophantic answers 95% of the time. Design the disagreement in: a flag, a counter-case, a refusal to smooth over weak reasoning just because the user sounds confident.
