Ask
Say who you are and what is in front of you. You will get questions to take to a visit, a huddle, a pitch, or a kitchen table.
A scoring error here becomes a body. The job is still the same: name the complication, prepare, proceed with care.
For I am a doctor, nurse, or clinician
Opening
If this goes well, who is in a position to say so? If it goes badly, who can be named?
Which of our current tools have we never put on this table?
Where is our evidence of unequal performance, and where have we not looked?
What is the smallest trial that would teach us, and what would make us stop?
Who is missing from this room who will live with the decision?
Same questions, denser rooms. Go when you are ready — not as a test of seriousness.
How to manage, not only name
U.S. National Institute of Standards and Technology
A voluntary framework for putting AI risk into an organisation's ordinary risk practice. Widely referenced; not a law.
You are past 'what can go wrong' and need a cycle: govern, map, measure, manage.
Laws and policies against risks
MIT × Georgetown CSET
More than a thousand governance documents, mapped to the same risk shelves. Filter by domain, sector, lifecycle, and who in the value chain is named.
You need to see which risks a law, standard, or policy actually covers — and which it misses.
Health-specific guidance
World Health Organization
WHO's 2024 guidance on large multimodal models in health: consent, evidence, equity, and the duties that remain with clinicians and states.
The question is not 'is AI risky?' but 'what does care require of this tool?'