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

Using Issues to Explain Legal Decisions