Framework supports legal decisions with explainable rule-based reasoning
A decision-support system applied to Law: Reasoning and explainability of the decision
Artificial Intelligence
Summary
As laws increasingly regulate how police handle digital data, making sure these rules are followed is important but complex. The authors designed a system that turns legal rules into clear computer instructions, so police agencies can follow them correctly. This system uses symbolic AI and queries to reason about the rules and explains how it reached each decision, building trust. If information is missing, the system can suggest what else to ask for to reach a good conclusion.
What this means in practice
- •For law enforcement agencies: Help officers and analysts follow complex data rules by providing explainable decisions based on formal legal norms.
- •For legal compliance teams: Support teams in verifying that data processing complies with regulations using a systematic, explainable reasoning tool.
Authors
Jeremy Bouche-Pillon, Pascale Zarat{é}, Yannick Chevalier, Nathalie Aussenac-Gilles
Abstract
The emergence of the digital transition brought an increasing need to control the processing of digital information, including in Law Enforcement Agencies (LEAs). At the EU level, in recent years, many regulations have emerged to control data processing and exchange. Texts other than the GDPR, such as the ''Law Enforcement Directive (LED)'', appeared to regulate specifically how Law Enforcement Agencies (LEAs) could process data. A formal representation of these regulations can be part of decision systems that support LEAs in processing data in compliance with the regulations. Although many new formalisms have emerged to represent legal norms and rules, few are provided with a reasoning mechanism. Furthermore, systems used in decision-making processes in critical contexts such as medical diagnoses or legal decisions cannot be fully automated, and the explainability of their results is essential to ensure user confidence in decisions. This explainability aspect, while crucial, is lacking in most modern approaches that rely on machine learning. This paper describes a framework to operate formal rules from regulations, by focusing on explainability of the decision. After describing the general architecture of the proposed decision support framework, the paper showcases how symbolic AI and the SPARQL query language can support legal reasoning. It then describes an algorithm to generate a justification for the reasoning results, and outlines the procedure to be followed when the reasoning does not lead to a satisfactory conclusion. We notably focus on a method based on decision trees to determine what additional information to request from the user.