Hosted by the Interactive AI Centre for Doctoral Training and the Intelligent Systems Lab

Abstract: This research presents first steps towards a generic approach to automatically translating legal text into machine-executable computational logic. We demonstrate how this approach can be used to automate public sector processes. Since automation of legal processes is a high-risk application of AI, we use explainable AI based on natural language processing using scope-restricted pattern matching and grammatical parsing. Extracted rules are converted to Prolog predicates and visualized as textual lists and graphical decision trees. Our developed Law as Code prototype has been evaluated as a proof-of-concept at the Austrian Ministry of Finance and successfully demonstrated the automatic extraction of explainable rules from the Austrian Study Funding Act. This validates our approach and suggests promising future research directions, most notably the prospect of integrating GenAI Large Language Models (LLMs) into the rule extraction process, while retaining provenance and explainability.

Bio: Simon Price received his Ph.D. in Computer Science from the University of Bristol, UK in 2014. He is Senior Director, Data & AI at Unisys and an Honorary Senior Research Fellow at the University of Bristol. He has over 50 peer-reviewed published articles spanning his research interests in e-Research and machine learning, including an invited cover feature in Communications of the ACM.

Contact iai-cdt@bristol.ac.uk if you would like lunch, served between 13.30-14.00