Technical Report on the Learning of Case Relevance in Case-Based Reasoning with Abstract Argumentation
This work addresses the need for cognitively tractable explanations in legal AI systems, but it is incremental as it builds on existing AA-CBR methods by integrating decision-tree-based learning.
The paper tackles the problem of automatically learning case relevance in case-based reasoning with abstract argumentation for legal prediction, showing that their method performs competitively with decision trees on two legal datasets and results in more compact representations.
Case-based reasoning is known to play an important role in several legal settings. In this paper we focus on a recent approach to case-based reasoning, supported by an instantiation of abstract argumentation whereby arguments represent cases and attack between arguments results from outcome disagreement between cases and a notion of relevance. In this context, relevance is connected to a form of specificity among cases. We explore how relevance can be learnt automatically in practice with the help of decision trees, and explore the combination of case-based reasoning with abstract argumentation (AA-CBR) and learning of case relevance for prediction in legal settings. Specifically, we show that, for two legal datasets, AA-CBR and decision-tree-based learning of case relevance perform competitively in comparison with decision trees. We also show that AA-CBR with decision-tree-based learning of case relevance results in a more compact representation than their decision tree counterparts, which could be beneficial for obtaining cognitively tractable explanations.