  Interpretable machine learning seeks to understand the reasoning process of
complex black-box systems that are long notorious for lack of explainability.
One flourishing approach is through counterfactual explanations, which provide
suggestions on what a user can do to alter an outcome. Not only must a
counterfactual example counter the original prediction from the black-box
classifier but it should also satisfy various constraints for practical
applications. Diversity is one of the critical constraints that however remains
less discussed. While diverse counterfactuals are ideal, it is computationally
challenging to simultaneously address some other constraints. Furthermore,
there is a growing privacy concern over the released counterfactual data. To
this end, we propose a feature-based learning framework that effectively
handles the counterfactual constraints and contributes itself to the limited
pool of private explanation models. We demonstrate the flexibility and
effectiveness of our method in generating diverse counterfactuals of
actionability and plausibility. Our counterfactual engine is more efficient
than counterparts of the same capacity while yielding the lowest
re-identification risks.
