Making digital intelligence second nature in regulated work.

Brightbeam’s mission is to integrate digital intelligence into regulated enterprises so that it becomes second nature. AI should be understood by the people who use it and connected to the systems and responsibilities already in place.

These are environments in which rules, expertise and consequence converge. An operator may recognise a change in a machine before an alarm. A reviewer may need more evidence before accepting a conclusion. Introducing AI means understanding how people make those judgements and keeping responsibility for consequential decisions clear.

How do we capture, structure and prepare expertise and judgement for AI? That means understanding what an expert noticed, when it matters and what would justify using it in another situation.

An accurate account of a decision still leaves its quality to be assessed. We need to understand what makes the judgement sound, where it might fail and how to recognise the difference. An unsuccessful decision might help expose a mistake in an evaluation, even when it would be unsuitable as advice.

These questions extend to the examples used to train and assess AI. They connect expertise capture to the work of defining what good performance means in a particular organisation.

CHAP and Metis are open research initiatives with papers and reference implementations to examine. Alongside them, work on evaluations asks how to assess the judgement reflected in AI behaviour.