I can agree this the comment. Linear models combined with advanced feature engineering gathered from domain knowledge can achieve great results in a white-box fashion!
A nice keynote by Vincent Warmerdam [1] talks about tips and tricking for advanced feature engineering combined with linear models.
A significant portion of ML workloads involve predicting or classifying something. Linear/logistic regression of the right variables/features typically gets a significant portion of the data's ability to predict /classify correctly, while being significantly easier to build, train, deploy, and understand.
Heck, in a large number of domains, simple ratios -- debt to income ratio in finance for example -- will dominate the feature weight for many models and can be used on their own as a pretty good heuristic.
A nice keynote by Vincent Warmerdam [1] talks about tips and tricking for advanced feature engineering combined with linear models.
[1] https://www.youtube.com/watch?v=68ABAU_V8qI