Using Issues to Explain Legal Decisions
This work targets the problem of interpretability in AI for legal professionals, but it appears incremental as it builds on existing AI and Law systems without introducing new methods or data.
The paper addresses the need for explainable AI in legal case outcome prediction by exploring explanations based on factor-based reasoning and precedent cases, focusing on the structural role of issues in cases.
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.