  Given a machine learning (ML) model and a prediction, explanations can be
defined as sets of features which are sufficient for the prediction. In some
applications, and besides asking for an explanation, it is also critical to
understand whether sensitive features can occur in some explanation, or whether
a non-interesting feature must occur in all explanations. This paper starts by
relating such queries respectively with the problems of relevancy and necessity
in logic-based abduction. The paper then proves membership and hardness results
for several families of ML classifiers. Afterwards the paper proposes concrete
algorithms for two classes of classifiers. The experimental results confirm the
scalability of the proposed algorithms.
