So I am thinking except to complete the modules, maybe it would be better to deliver in a more intuitive way.
totally, I initially conceived themis-ml as simply an extension of the sklearn interface to be able to handle the abstractions required to do fairness-aware ML, I think the delivery in the form of visualizations/dashboard would be super useful
we can probably chat about this more in detail, I think the main challenge for these kinds of more automated interfaces is the problem of over-generalizing an interface before knowing where the bulk of the use cases lie.
in this way there's a trade-off between lower-level procedures (as you mention grasping the logic of the underlying methods) and having an interface that abstracts all of those things away (much like the
predict, etc. methods in sklearn do), except at an even higher level (such that the user doesn't really need to know the underlying logic of the methods)
themis-mlas only providing the sklearn-like high-level interface to lower-level fairness-aware ML methods, and having another library altogether be responsible for visualizing the results of fairML methods.
themis-mlas providing an additional interface to gather insights from a particular set of models, e.g. seeing how a fairML method decreases social bias with respect to some naive method
themis-mlmodels in a more digestible way... would that be correct for me to say?