Modelling Value-oriented Legal Reasoning in LogiKEy
This work addresses the problem of formalizing legal reasoning for legal professionals and AI researchers, but it is incremental as it builds on existing LogiKEy methodology.
The paper applied the LogiKEy framework to model legal balancing using context-dependent value preferences, formalizing and automatically evaluating property law cases in Isabelle to demonstrate a testbed for legal domain-specific languages.
The logico-pluralist LogiKEy knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing in which legal knowledge (cases and laws) is encoded by utilising context-dependent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (involving appropriation of wild animals) within the Isabelle proof assistant system, illustrating how LogiKEy can harness interactive and automated theorem proving technology to provide a testbed for the development and formal verification of legal domain-specific languages and theories. Modelling value-oriented legal reasoning in that framework, we establish novel bridges between latest research in knowledge representation and reasoning in non-classical logics, automated theorem proving, and applications in legal reasoning.