Industrial safety for algorithmic systems
A governance posture that treats high-stakes AI-enabled workflows the way industrial safety treats consequential physical systems: with named hazards, evidence trails, stop-work authority, escalation routes, independent inspection, and a refusal to let smooth output stand in for verified control. The posture is not restriction-by-default; it also challenges safety measures that remove support, context, or agency without evidence that the withdrawal helps. Distinct from generic AI ethics by its insistence on operational primitives rather than principles.
We treat AI in serious workflows the way the safety inspector treats a factory floor. There are hazards. There are stop buttons. There is paperwork. The point is not to prevent the machine from working. It is to make sure a person can pause it when something is wrong, and also to make sure nobody calls a shutdown 'safety' without checking who it helps and who it hurts.
If your team is being asked to trust an AI-enabled decision (a vendor's risk score, an LLM-generated audit summary, a workforce optimisation tool), and there is no clear answer to 'who can pause this and where does the evidence live,' this is the practice that answers those questions before the next meeting.
