Because it's difficult. Bayesian methods don't scale well to the number of parameters that most modern ML demands. Doing inference for large models is intractable.

However, even if you're able to do that, the problem isn't solved. You have to answer level of confidence for what? The uncertainty / confidence that you get assumes your model is right. No model can tell you whether it is a true reflection of reality. I had written more about this on my twitter: https://twitter.com/paraschopra/status/1075033048767520768

Yes, you can create confidence estimates for both large neural networks and gradient boosting (see for instance the thesis of Yarin Gal). This covers the majority of commercial and academic applications.

ML is actually a field with very high standards for replication, in part because emperical results are currently the focus. If certain methods don't generalize to other datasets, then all bets are off: you are dealing with data that violates the IID assumption. No statistics, bean counting, or ML is going to help you get significant results.

They should, it shouldn't be difficult. I think the issue is more that ML gives more tools that a reasearcher could employ to get any arbitrary result if they knew what they are looking for.

Yeah, as most people here wrote already, it's statistics. And your models will yield those statistics...

Unfortunately if you're an engineer/physicist/chemist/biologist/social-scientist your background in statistics is neither fresh nor deep. So your professor comes to you: can you do something with ML, it's such a hot topic (your boss has also no background in statistics nor do his peers (which review your stuff...)) you say: yes (because a no I don't know about it won't be good for you). Then you go to some google or blockchain sponsored-tutorial where some self-taught-Indian-CS-Bachelor is telling you how to use ML with Python and Tensorflow. You might wonder about some things but in the end you need to get things done and feed your data (which is often garbage, but verifying that it's not is not hot) into an algorithm you don't understand. Then you find some other guys, doing this, cite them and publish. 0 scientific value generated, but a great step for your academic carrer nonetheless.