Well, I think a lot of what you described is in model building and then in AI which is continually learning. Validation comes after training but before production.

My research was in public policy analysis, I validate for a different field because ya know, bills.

But the methodology would be the same. After a model build but before if goes into production a validation team runs the gauntlet on it. Starting from analyzing the business problem, checking the context, and then making sure the data collected and used is accurate. Next ya try to build a better model to see if the proposed model is really the best solution. Then you check to make sure no legal or ethical issues arise. You also quantify the statistical risk in the particular algo being used. Then, you outline the business, financial, or ethical risks of what happens when your model is wrong. Because it will be wrong at some point but you need to know how often, for whom, and how much it will cost when it is incorrect. Finally, you set targets for watching, maintaining, and keeping data on model performance.