Users run a private simulation of what your AI can do — and every acceptance, every surprise, every collapse of trust traces back to whether that model is honest. How mental models form, why intent-based interfaces raise the stakes, the sycophancy trap, and the onboarding/empty-state moves that seed an accurate model instead of a hype one.

• →Human-AI Mental Models
• →Uncertainty and Error UX

Probabilistic systems are wrong on a schedule, and the wrong 1% is adversarial, not random. The four-family error taxonomy and the distinct UX each demands, confidence display that calibrates instead of decorating, graceful-failure screens you design first, and the expectation-setting + ceding-control playbook — grounded in Galactica, Air Canada, AI Overviews, and Bing Sydney.

• →Uncertainty and Error UX
• →Human-AI Mental Models

Trust is calibrated, not granted — it rises when behavior matches the user’s model and falls when it diverges. The transparency primitives that move it (explanations, citations, showing the work), the over- vs under-reliance curve and its measured costs, and the cognitive-forcing moves that beat automation bias — grounded in Perplexity, the o1-vs-Claude reasoning split, and Anthropic’s Citations API.

• →Trust and Transparency
• →Human-AI Mental Models

Automation creates a “commit moment” heavier than manual work — running an AI command feels like delegating, not authoring. The three control primitives (undo, edit, override), classifying every AI action on a commitment axis, choosing the right level of automation, and the feedback loops that keep humans in charge — grounded in Copilot’s three-tier acceptance and the Smart Reply / Magic Editor control failures.

• →Human Control and Feedback
• →Human-AI Mental Models

Responsibility is a front-page concern, not a footer; accessibility is the gate for probabilistic UIs, not an after-market patch. IBM’s six pillars and Carbon’s shipped AI-label component, the probabilistic-UI accessibility checklist, the bias mechanism (Amazon’s scrapped recruiter), and the data-rights and human-escape-hatch moves — grounded in alt-text generation’s win-and-limit and ChatGPT’s screen-reader regressions.

• →Responsible and Accessible AI
• →Human-AI Mental Models

Put the whole track to work: a repeatable audit method (the four-pillar review + the timeline walk), a worked critique-and-redesign of a real support chatbot against every principle, and the portfolio/interview craft to present it — the five-stage AI design whiteboard format, the rubric interviewers grade, and what a standout AI portfolio piece actually shows.

• →Human-AI Mental Models
• →Uncertainty and Error UX
• →Trust and Transparency