Automated legal reasoning with discretion to act using s(LAW)

TL;DR

The paper tackles automating legal reasoning under vagueness and discretion, emphasizing the need for human-understandable justifications. It proposes s(LAW), a framework that uses a top-down predicate ASP engine (s(CASP)) to model ambiguity and discretion in law. Contributions include translation patterns from law to ASP, the s(LAW) architecture (ArticleESO.pl, ArticleESO.pre.pl, Students.pl), and a Madrid CM ESO running example with natural-language explanations. s(LAW) produces explainable, multiple-model inferences (partial models) and supports automated verification of regulated issues while presenting lawful discretionary options.

Abstract

Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the "Comunidad de Madrid".

Figures (2)

• Figure 1: Translation of the procedure for awarding school places under s(LAW).
• Figure 2: First lines of the file .