39 Principles for Designing Human-AI Interaction
Applied principles for building AI interfaces with clear evidence, user control, recoverable failure, and bounded autonomy
Taras Bakusevych turns research on human-AI interaction, mixed-initiative systems, trust in automation, and responsible AI into 39 product principles. The framework covers probabilistic foundations, expectation setting, calibrated trust, transparency, control and agency, graceful failure, co-creation, responsible autonomy, and sustained reliance. Use it as a design review for the full AI interaction cycle. It covers what the system should explain, when it should ask or act, how users check output, and how they recover when the system is wrong.
Use this as a product review before development or release. Check whether the interface shows its AI role, sources, limits, plan, and important state changes. Make acceptance, correction, rejection, undo, and escalation easy. Increase approval and review requirements as actions become less reversible or more consequential.
Why it is here. It connects established human-AI research to interface decisions that a product team can inspect in a design review.
- 39 applied human-AI principles
- provenance over confidence scores
- control and recovery patterns
- autonomy tied to stakes and reversibility
- design for model and data changes
Verified 2026-09-10. HTTP 200. https://syntaxstream.substack.com/p/42-principles-for-designing-humanai returned 200. The live page is dated 2026-06-30 and now presents 39 numbered principles, although the URL slug still says 42. The framework cites mixed-initiative interaction, the Google PAIR Guidebook, IBM generative AI principles, Microsoft human-AI guidelines, and current model guidance. Checked 2026-09-10.
