Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

A model-based approach to the automatic revision of secondary legislation

Li, Tingting, Balke, Tina, De Vos, Marina, Padget, Julian and Satoh, Ken 2013. A model-based approach to the automatic revision of secondary legislation. Presented at: ICAIL 2013: XIV International Conference on Artificial Intelligence and Law, Rome, Italy, 10-14 June 2013. ICAIL '13: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law. New York, NY, USA: ACM, pp. 202-206. 10.1145/2514601.2514627

Full text not available from this repository.

Abstract

Conflicts between laws can readily arise in situations governed by different laws, a case in point being when the context of an inferior law (or set of regulations) is altered through revision of a superior law. Being able to detect these conflicts automatically and resolve them, for example by proposing revisions to one of the modelled laws or policies, would be highly beneficial for legislators, legal departments of organizations or anybody having to incorporate legal requirements into their own procedures. In this paper we present a model based approach for detecting and finding legal conflicts through a combination of a formal model of legal specifications and a computational model based on answer set programming and inductive logic programming. Given specific scenarios (descriptions of courses of action), our model-based approach can automatically detect whether these scenarios could lead to contradictory outcomes in the different legal specifications. Using these conflicts as use cases, we apply inductive logic programming (ILP) to learn revisions to the legal component that is the source of the conflict. We illustrate our approach using a case-study where a university has to change its studentship programme after the government brings in new immigration regulations.

Item Type: Conference or Workshop Item (Paper)
Status: Published
Schools: Computer Science & Informatics
Publisher: ACM
ISBN: 9781450320801
Last Modified: 15 Apr 2020 13:47
URI: https://orca.cardiff.ac.uk/id/eprint/130986

Citation Data

Cited 18 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item