Those are the expert systems that have fallen out of favour over a decade ago, so thanks, but no thanks.
> many ML models today are either full on white boxes or are directly interpretable in various ways
Sources please, and remember to use relevant sources only, i.e. interpretation of medical image analysis like here [1].
Notably activation maps told researchers precisely nothing about what the neural net was actually basing its conclusions on:
> For other predictions, such as SBP and BMI, the attention masks were non-specific, such as uniform ‘attention’ or highlighting the circular border of the image, suggesting that the signals for those predictions may be distributed more diffusely throughout the image.
So much for "fully white boxes" and "direct interpretability"...
[1] https://storage.googleapis.com/pub-tools-public-publication-...
ML is not the black box nightmare that I see it described as on here. You can figure out feature contributions and can quite easily (and accurately) verify it's reasoning. If you really need these kind of models, look into various kinds of tree based ML models like random forests or boosted trees...