Thanks for the reply! I don't really have a good frame of reference for the model-improvement feedback loop in different industries, so I could very well be gen

Thanks for the reply! I don't really have a good frame of reference for the model-improvement feedback loop in different industries, so I could very well be generalizing incorrectly. As for the experts part, I assume there is some expert out there who would know enough about the economy/trade at the time that they could glean enough information about materials, dyes, etc. to make an educated guess/ Faith in results - while maybe in this simple example that's valid, what about in cases where ethics are concerned? IIRC there was a police force using an ML model to predict where units should be patrolling and it turned out they were overpolicing primarily black neighborhoods. Same thing with ML systems for employee applicant screening. Communication - I think that this is a fair point, although given how pervasive ML is at this point I think it's good to try make sure the view of the public is not too skewed (like the whole "God particle" fiasco several years ago) Either way, thanks for the reply! You make some good points and I don't think many of my questions were well-framed enough.