On Deciding Feature Membership in Explanations of SDD & Related Classifiers

Understand

When reasoning about explanations of Machine Learning (ML) classifiers, a pertinent query is to decide whether some sensitive features can serve for explaining a given prediction.

• Recent work showed that the feature membership problem (FMP) is hard for $\Sigma_2^P$ for a broad class of classifiers.
• In contrast, this paper shows that for a number of families of classifiers, FMP is in NP.
• Concretely, the paper proves that any classifier for which an explanation can be computed in polynomial time, then deciding feature membership in an explanation can be decided with one NP oracle call.