Well there's the field of "statistics".

"Data Science" seems to just generally include everything that starts with data and ends with new information.

Back when it was called "Data Mining", there was a particular aspect called "Knowledge Discovery". KDD = knowledge discovery from databases, which one of the biggest ML conferences is named after.

I view it as:

Explainable models = statistical models (regression / multilevel models).

Black box models = machine learning (tree based models).

AI = black box models for computer vision / NLP.

hmm Black box models - refers to NeuralNets that build iteratively "unsupervised" .. confusion reading this because, tree-based models sounds similar to other, non-NN models such as RandomForest.. a RandomForest model is reproducible and may be very explainable, depending on the way the setup was done "and other factors"

AI = long history of multiple meanings of this term, but a common one is "get computers to solve a question that used to be only solveable by a human".. not at all specific to vision 'slash' natural language processing

I don't know why trees are black box when partial dependence has existed for 20 years.

The general activity is called Exploratory Data Analysis (EDA).

Not sure if there's another name for "EDA using ML".