  The need to explain the output from Machine Learning systems designed to
predict the outcomes of legal cases has led to a renewed interest in the
explanations offered by traditional AI and Law systems, especially those using
factor based reasoning and precedent cases. In this paper we consider what sort
of explanations we should expect from such systems, with a particular focus on
the structure that can be provided by the use of issues in cases.
