Feature Necessity & Relevancy in ML Classifier Explanations

TL;DR

This paper investigates feature necessity and relevancy in ML classifier explanations, relating these concepts to logic-based abduction, and proposes scalable algorithms with experimental validation.

Contribution

It introduces a formal framework for analyzing feature necessity and relevancy in explanations, and provides algorithms for specific classifier classes with proven scalability.

Findings

Proves complexity results for relevancy and necessity problems in various classifiers.

Develops concrete algorithms for two classifier classes.

Experimental results confirm algorithm scalability.

Abstract

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.