A Modal Logic for Temporal and Jurisdictional Classifier Models
This work addresses verification challenges for ML classifiers in the legal domain, representing an incremental advancement in logic-based models.
The authors tackled the problem of verifying machine learning classifiers in legal case-based reasoning by introducing a modal logic that incorporates temporal dimensions and court hierarchies to resolve conflicts between precedents.
Logic-based models can be used to build verification tools for machine learning classifiers employed in the legal field. ML classifiers predict the outcomes of new cases based on previous ones, thereby performing a form of case-based reasoning (CBR). In this paper, we introduce a modal logic of classifiers designed to formally capture legal CBR. We incorporate principles for resolving conflicts between precedents, by introducing into the logic the temporal dimension of cases and the hierarchy of courts within the legal system.