I wonder if "synthetic data" could be used to proof ML? Ie, rather than start from a huge data set that you _think_ is clean enough to know what a Car is (or w/e), you could use a data set that is perfectly clean and the ML set could be tweaked to give the expected result.

Of course, that would still be hairy as you'd have to ensure your ML still performed on real data sets. All the clean data would do is allow you to write unit tests of sorts for your ML with more confidence than the all-too-common unclean real data.

No idea, just making stuff up. Interesting thought.