DIGITAL & IT | AI & SAFETY
Finally, he says that “safety cases are not just done by
one person and signed off, they go through lots of different stakeholders.” With a large number being done in parallel, “at any one time you want some kind of system that will be able to track where every document is at any moment in time.” What stakeholders have seen it? What parts have been reviewed? What are the comments that have been made? What adaptations have been made? Where is the audit? Who has approved what, and when? This becomes very important when considering workflow
Above:
Using AI the user can ask unstructured questions using normal syntax, not coding
like searching for a keyword, Prettejohn explains: “It is the
context that is interesting, not the keyword, so it is much more effective to do a semantic search than to search for key words. You find all the different applications where the word is used and see that some are useful.” This is about saving time, rather than cost. “Look at
what goes in to building a nuclear reactor and the cost and saving head count is not the thing that worries you... But being able to reference the old and bring it into the new is important.”
Another aim is document validation and curation. “Safety
cases are hundreds of documents and thousands of pages and you need to be able to reference documents to one another.” Making changes in one document, making sure they are made in all the other documents and ensuring they are up to date in them all – is “a really difficult thing to do.” Prettejohn says, “These documents are not just written by one person. Even if it is drafted by one person there are revisions and revisions, written by different stakeholders who are required to go through a safety case before it is approved. So understanding when a safety case has been revised is really important, and so is looking through two variants of a safety case to look at what has been revised and why. Then you want to be able to apply those learnings to any future safety cases you are generating, to accelerate that process.” He takes the opportunity a step further, in the context of hundreds of safety cases being written. Talking about a national programme such as the UK’s, he asks, “If you could bring them into a central platform and use those features, are you able to generate safety case documentation from scratch? “You always need humans in the loop and humans should always be actively employed in writing safety cases, but there is a lot of safety case content that has nothing to do with the specifics of what is being approved. There is lots of generic stuff that has to go in and lots of risks that are relevant through lots of different safety cases.” Taking all the context of previous safety cases, you would
ask AI to make a new safety case that looks broadly like something existing, and pull in all the relevant content. That way, “You don’t have to start from scratch, you have a scaffold… and we all know that the hardest part of writing documents is starting. Being able to mark someone else’s homework is a lot easier.”
22 | June 2024 |
www.neimagazine.com
during construction. Large sites like nuclear plants have thousands of people and complex information that takes days or weeks to review. Any large capital project can build up a stack of ‘to approve’ documents and undocumented work in progress. What is more, doing something differently – and better – requires safety case approval. But people don’t have the tools to compare and contrast sections on several documents and there is no simple way to say ‘these are the points you have to look at’. However, AI can answer an unstructured question such as ‘can I excavate here?’ by interrogating all the documents that relate to that square metre of land.
Reasoning not regulation In more general terms, Prettejohn says, “What we are trying to teach the AI isn’t regulation. We are trying to teach reasoning.”
He uses an analogy from a much simpler process – checking invoice details against contract terms. “We don’t train it on every contract because that’s not verifiable. Instead, we teach it how to do it. This is all logic relationships.” Translating that to a safety case example, “you would
have all the regs, split out clause by clause. If you have any case history you would bring that in as another object type, you can bring in lawsuits – here is where regulators have taken action.” He says, “The point is to use the AI for what it is good at, which is essentially pretending to reason about things, and not use it for what it is bad at, which is remembering facts.” He goes back to his analogy of AI as a colleague: “It is
never tired and if you don’t like the outcome you can get it to do it again, so you have the opportunity to revise as often as you like.” Prettejohn also talks about the ‘tribal knowledge’ that arises in a department and accessing the expertise and experience locked away in people’s heads. “New people coming in have to spend many years building up that knowledge,” he says, “It is inaccessible and without years of shadowing them you won’t be as effective as them, because the knowledge is locked away in their head – however good they are at telling someone else, that will always be imperfect.” That affects the ability of the organisation to be agile, or to bring on new members of the team to be as good as the individuals who have been around a long time. “With that tribal knowledge encoded I could turn up tomorrow and although I know zero about a nuclear safety case, if I had something like this with all that logic built in already, I wouldn’t be completely ineffective for three years trying to learn it. You could be at least semi effective after a couple of weeks or months. “When you look at it in those terms it is industry- changing.” ■
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49
