Explanation and justification of legal decisions has become a highly relevant topic in light of the explosion of interest in the use of machine learning (ML) approaches to predict legal decisions. Current suggestions are to use the established factor based explanations developed in AI and Law as the basis for explaining such programs. We, however, identify factor ascription as an important aspect of explanation of case outcomes not currently covered, and argue that explanations must also include this aspect. Finally, we outline our proposal for a hybrid system approach that combines ML and Abstract Dialectical Framework (ADF) layers to engender an explainable process.