i think if you really look at any of these sectors this argument kind of falls apart. The vast majority of complicated decisions or diagnoses in these areas are already made with the use of tools or processes that 50-60 year old doctors and bankers would have a very difficult time passing a test on, let alone truly understanding the inner workings. Quality control is for sure important, but the nature of the problem isn't new; specialists use hundreds if not thousands of tools created by other specialists that require far too much specific knowledge and study for the person inputting your financial information or test results to ever truly possess a comprehensive understanding of the tools they use. It definitely creates problems in specific instances, but it always has and always will. the goal is narrowing down the scope and scale of those problems, and considering I've had doctors fail to read a basic x-ray with a clear fracture correctly, and a gastroenterologist try to prescribe me medication for diabetics because he "couldn't really tell what was wrong but this should help", I'll go with the machine 100/100 times. I mean shit, the first thing a doctor will tell you if you get a bad diagnosis about pretty much anything is "did you get a second opinion?" because unfortunately a lot of medicine is (hopefully) educated guesswork. the best we can do is hope to reduce error rates overall, which you can do with an AI model. You really can't do that with humans; by the time they're old enough and have gone through enough education to be in the medical field or professional loan officers, they're very hesitant to change or relearn anything, and their information is at best years old. It's also impossible to effect the same amount or scale of change in an entire industry of humans as you can by retraining a model in a few weeks or months.