#responsible AI
8 pieces filed under “responsible AI”.
Personalisation vs privacy: drawing the line deliberately
Draw the personalisation privacy line before legal does: on-device and anonymous patterns, explainable personalisation, and consent UX that actually means something.
Disclosure patterns: telling users when a machine is speaking
AI disclosure that survives legal review and user testing: label placement, export watermarking, model transparency, and wording that informs, not alarms.
Our responsible-AI review, in the open
The full review we run before any AI feature ships: risk taxonomy, red-team scripts, disclosure patterns, data-handling checklists — and how we say no to features.
Designing AI features users can trust
Trust is an interface property, not a policy page. Citations, confidence, correction affordances and honest capability copy — how we design AI people rely on.
When the AI says no: moderation UX
Every generative AI feature eventually refuses a user, and most refusals are dead ends. Designing blocked, revised and appeal states that preserve dignity.
When the model is wrong: designing failure states for AI features
AI failure is a design state, not an exception. Wrong-answer UX, graceful degradation tiers, feedback that improves the system, and copy that keeps trust intact.
Beacon Health: an AI triage assistant that knows when to stop talking
A primary-care network wanted an AI front door without the risk. We designed an AI triage assistant with red-flag escalation, clinician review and offline evals.
Case studyPylon Care: an AI support assistant that knows its limits
Pylon Health's support queue drowned in the same five questions. We shipped an AI assistant with citations, red lines and a kill switch — evals first.