On Deciding Feature Membership in Explanations of SDD & Related Classifiers

TL;DR

This paper investigates the computational complexity of determining whether features are part of explanations for classifiers, showing that for certain classifiers, this problem is in NP and proposing efficient propositional encodings with practical benefits.

Contribution

The paper demonstrates that feature membership decision problems are in NP for classifiers with polynomial explanation computation and introduces propositional encodings for SDDs and related languages.

Findings

Deciding feature membership is in NP for certain classifiers.

Propositional encodings enable efficient practical decision-making.

Experimental results confirm the approach's efficiency.

Abstract

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. The paper then proposes propositional encodings for classifiers represented with Sentential Decision Diagrams (SDDs) and for other related propositional languages. The experimental results confirm the practical efficiency of the proposed approach.