UX Collective: 39 principles for designing human-AI interaction

Taras Bakusevych’s June 2026 article in UX Collective offers a framework of 39 principles for designing interfaces that include AI components. The article starts from a premise that most practicing designers recognize: traditional UI is built around predictable, deterministic behavior, and AI systems behave differently. The same input can produce different outputs. The system can be wrong in plausible-sounding ways. That gap requires a different design vocabulary.

What the article covers. The 39 principles are organized around four main concerns: appropriate reliance, user control, transparency, and responsible autonomy. Each principle addresses a practical decision a designer has to make: when should the system suggest something versus wait to be asked? How should uncertainty appear on screen? What evidence should accompany a generated answer? When an AI action is consequential, how much autonomy should it have without explicit approval?

Bakusevych argues that product quality in AI interfaces is not determined by model capability alone. A highly capable model placed behind a bad interface produces a bad product. Design work is responsible for communicating what the system can and cannot do, for giving users tools to verify and override, and for making error recovery feel natural rather than alarming.

The framework distinguishes between contexts where AI is strongest — messy inputs, ambiguous intent, synthesis of large bodies of information, content generation — and contexts where it is weakest: tasks requiring exactness, repeatability, or deterministic control. Recognizing this boundary matters for deciding which features should be AI-driven at all, and which should remain rule-based.

Key takeaways. Several principles deal specifically with uncertainty communication: the article argues that making uncertainty visible is preferable to projecting false confidence, even when it makes the product feel less polished. Similarly, one recurring theme is that evidence should accompany generated answers — not as a footnote, but as part of the primary interface, so users can check the system’s reasoning without leaving the flow.

The principles around autonomy are particularly useful for teams building agents or workflows with consequential outputs. The framework suggests calibrating autonomy to the stakes of the action: low-stakes, reversible tasks can be acted on without explicit user approval, while irreversible or high-stakes actions should require confirmation or at minimum make the pending action visible before it executes.

Who it is useful for. Product designers working on AI features who need a structured way to approach interface decisions. Useful also for product managers who want a shared vocabulary with their design team when discussing AI behavior. The framework is tool-agnostic and does not assume any specific AI stack, which makes it applicable across different product types.

The full article is available on UX Collective. The principles are dense enough that returning to specific sections as new design problems come up is more practical than reading front to back.