Poster comment here. I spent time in the banking/finance world where "Model Risk Management" is a huge thing. I think some of that can be used to think about

Poster comment here. I spent time in the banking/finance world where "Model Risk Management" is a huge thing. I think some of that can be used to think about the risks/controls. It focuses on all the things that can go wrong - a) model doesn't produce an expected result and b) model is used inappropriately. So, they do things like, performing a model validation which includes rigorous qualitative and quantitative tests, setting limitations on use, testing the sensitivity to inputs and boundary conditions, among many others. In the traditional sense, a machine learning model is a new beast. In most cases, these models don't have an agreed upon "methodology" (i.e. neural networks) and therefore don't have an "expected" result that can be tested (especially for unsupervised learning models). So, curious to know what others are thinking?