Two fields required. Full details optional. Reference number returned immediately.
Recent incidents
The full record, anonymised before publication
Each incident classified using the MIT AI Risk Repository causal and domain taxonomies. Reports with linked outcomes shown in green.
AI products with linked incident reports
Medicines and Healthcare products Regulatory Agency (MHRA) registration status cross-referenced. Use before procurement or when authoring clinical safety cases under DCB0129 (the NHS clinical risk management standard for digital health vendors).
Issued where patterns indicate systemic risk
Required action with deadline, cross-referenced to originating incidents. Closing the loop is what changes the reporting calculation.
Signal, not noise
Mapped against the MIT Domain Taxonomy (7 domains, 24 subdomains). airisk.mit.edu
Where the silence is loudest
Five Yellow Card reports across 15M+ ambient voice tool consultations is a measurement failure, not a safety record.
Named accountability, publicly visible
A named trust AI safety lead is the minimum unit of governance that makes learning from this registry possible.
Minimum friction, maximum signal
Two fields required. Full details optional. Designed for the clinician who finishes surgery and notices something off. This registry accepts reports from patients too.
+ Add full details (optional but valuable)
Report submitted
Reference:
Reviewed by the clinical safety panel within 5 working days. You will be updated when it is published — closing the loop on what happened next.
Theoretical foundation & operating principles
This registry complements — does not replace — LFPSE, MHRA Yellow Card, and trust-level incident reporting. Classification grounded in the MIT AI Risk Repository.