Identification of Legislative Errors
Abstract
We present an approach designed to support the process of legislative drafting by helping to detect errors in a normative text. It is based on a framework allowing for representation and comparison of structure and semantic content of legal provisions. Such comparison serves as a starting point for detection of (potential) legislative errors. The approach provides in particular criteria to select provisions to be compared, related to the phenomenon of provisions overlapping. We show that specific cases of such an overlap may amount to legislative errors. The presented framework enables a precise and transparent account of these errors. We also acknowledge that textual provisions enable various interpretations, while the error methodology detection assumes that the semantic representation of provisions is a result of a specific interpretation. We introduce the notion of Constraining Interpretive Rules which are used to evaluate the acceptability of specific interpretations of legal provisions. We discuss the features of the model on a real example and we present an implementation of the approach by using semantic technologies.
References
[1]
T. Agnoloni, L. Bacci, E. Francesconi, W. Peters, S. Montemagni, and G. Venturi. 2009. A two-level knowledge approach to support multilingual legislative drafting. In Law, Ontologies and the Semantic Web, J. Breuker, P. Casanovas, M. Klein, and E. Francesconi (Eds.). Frontiers in Artificial Intelligence and Applications, Vol. 188. IOS Press, Amsterdam, The Netherlands, 177--198.
[2]
L. Allen. 1962. Some Uses of Symbolic Logic in Law Practice. MULL: Modern Uses of Logic in Law 3, 2 (1962), 119--136.
[3]
G. Antoniou, D. Billington, G. Governatori, and M.J. Maher. 1999. On the modeling and analysis of regulations. In Proceedings of the Australian Conference Information Systems. Victoria University of Wellington, New Zealand, 20--29.
[4]
Michal Araszkiewicz, Enrico Francesconi, and Tomasz Zurek. 2021. Identification of Contradictions in Regulation. In Legal Knowledge and Information Systems - JURIX 2021: The Thirty-fourth Annual Conference, Vilnius, Lithuania, 8-10 December 2021 (Frontiers in Artificial Intelligence and Applications, Vol. 346), Schweighofer Erich (Ed.). IOS Press, 151--160. https://doi.org/10.3233/FAIA210331
[5]
Michal Araszkiewicz and Tomasz Zurek. 2015. Comprehensive Framework Embracing the Complexity of Statutory Interpretation. In Legal Knowledge and Information Systems - JURIX 2015: The Twenty-Eighth Annual Conference, Braga, Portual, December 10-11, 2015. 145--148. https://doi.org/10.3233/978-1-61499-609-5-145
[6]
Michal Araszkiewicz and Tomasz Zurek. 2016. Interpreting Agents. In Legal Knowledge and Information Systems - JURIX 2016: The Twenty-Ninth Annual Conference. 13--22. https://doi.org/10.3233/978-1-61499-726-9-13
[7]
Kevin Ashley. 2017. Artificial Intelligence and Legal Analytics: New Tools for Law Practice in the Digital Age. Cambridge UP.
[8]
C. Biagioli. 2009. Modelli Funzionali delle Leggi. Verso testi legislativi autoesplicativi. Legal Information and Communications Technologies Series, Vol. 6. European Press Academic Publishing, Florence, Italy.
[9]
C. Biagioli, A. Cappelli, E. Francesconi, and F. Turchi. 2007. Law Making Environment: perspectives. In Proceedings of the V Legislative XML Workshop. European Press Academic Publishing, 267--281.
[10]
C. Biagioli, E. Francesconi, A. Passerini, S. Montemagni, and C. Soria. 2005. Automatic semantics extraction in law documents. In ICAIL. 133--139.
[11]
Julie Dickson. 2016. Interpretation and Coherence in Legal Reasoning. In The Stanford Encyclopedia of Philosophy (winter 2016 ed.), Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University.
[12]
T. Endicott. 2000. Vagueness in Law. Oxford University Press.
[13]
E. Francesconi. 2014. A Description Logic Framework for Advanced Accessing and Reasoning over Normative Provisions. International Journal on Artificial Intelligence and Law 22, 3 (2014), 291--311.
[14]
E. Francesconi. 2016. Semantic Model for Legal Resources: Annotation and Reasoning over Normative Provisions. Semantic Web journal: Special Issue on Semantic Web for the legal domain 7, 3 (2016), 255--265.
[15]
E. Francesconi and A. Passerini. 2007. Automatic Classification of Provisions in Legislative Texts. International Journal on Artificial Intelligence and Law 15, 1 (2007), 1--17.
[16]
Fumihito Nishino Ha-Thanh Nguyen, Fungwacharakorn Wachara and Ken Satoh. 2022. A Multi-Step Approach in Translating Natural Language into Logical Formula. In Legal Knowledge and Information Systems - JURIX 2022: The Thirty-Fifth Annual Conference, Saarbrucken, Germany, December 14-16, 2022. 103--112. https://doi.org/
[17]
R. Hoekstra, J. Breuker, M. di Bello, and A. Boer. 2009. Lkif core: Principled ontology development for the legal domain. In Law, Ontologies and the Semantic Web, J. Breuker, P. Casanovas, M. Klein, and E. Francesconi (Eds.). Frontiers in Artificial Intelligence and Applications, Vol. 188. IOS Press, The Netherlands, 21--52.
[18]
Wesley Newcomb Hohfeld. 1913. Some Fundamental Legal Conceptions as Applied in Judicial Reasoning. I. Yale Law Journal 23 (1913), 16--59.
[19]
Wesley Newcomb Hohfeld. 1917. Some Fundamental Legal Conceptions as Applied in Judicial Reasoning. II. Yale Law Journal 26 (1917), 710--70.
[20]
F. Macagno, D. Walton, and G. Sartor. 2012. Argumentation Schemes for Statutory Interpretation. In Argumentation. International Conference on Alternative Methods o Argumentation in Law., M. Araszkiewicz, M. Myska, T. Smejkalova, J. Savelka, and skop M. (Eds.). Brno, 61--76.
[21]
N. MacCormick and R. Summers. 1991. Interpreting Statutes. A Comparative Study. Ashgate, Dartmouth.
[22]
Libal T. Novotna, T. 2022. An Evaluation of Methodologies for Legal Formalization. In Explainable and Transparent AI and Multi-Agent Systems. 4th International Workshop, EXTRAAMAS 2022, Virtual Event, May 9-10, 2022, Revised Selected Papers (virtual event) (EXTRAAMAS 2022). Springer, Cham, Switzerland, 189--203. https://doi.org/book/10.1007/978-3-031-15565-9
[23]
G. Sartor. 2006. Fundamental Legal Concepts: A Formal and Teleological Characterisation. Artificial Intelligence and Law 14, 1-2 (2006), 101--142.
[24]
Jaromír Savelka and Kevin D. Ashley. 2020. Learning to Rank Sentences for Explaining Statutory Terms. In Proceedings of the Fourth Workshop on Automated Semantic Analysis of Information in Legal Text held online in conjunction with the 33rd International Conference on Legal Knowledge and Information Systems, ASAIL@JURIX 2020, December 9, 2020 (CEUR Workshop Proceedings, Vol. 2764), Kevin D. Ashley, Katie Atkinson, Luther Karl Branting, Enrico Francesconi, Matthias Grabmair, Vern R. Walker, Bernhard Waltl, and Adam Zachary Wyner (Eds.). CEUR-WS.org. http://ceur-ws.org/Vol-2764/paper7.pdf
[25]
Jaromir Savelka, Huihui Xu, and Kevin D. Ashley. 2019. Improving Sentence Retrieval from Case Law for Statutory Interpretation. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (Montreal, QC, Canada) (ICAIL '19). Association for Computing Machinery, New York, NY, USA, 113--122. https://doi.org/10.1145/3322640.3326736
[26]
Robert van Doesburg and Tom van Engers. 2019. The False, the Former, and the Parish Priest. In Proceedings of the Seventeenth International Conference on Artificial Intelligence and Law (Montreal, QC, Canada) (ICAIL '19). Association for Computing Machinery, New York, NY, USA, 194--198. https://doi.org/10.1145/3322640.3326718
[27]
Robert Van Kralingen. 1995. Frame-based Conceptual Models of Statute Law. Kluwer.
[28]
Pepijn R. S. Visser, Robert W. van Kralingen, and Trevor J. M. Bench-Capon. 1997. A Method for the Development of Legal Knowledge Systems. In Proceedings of the 6th International Conference on Artificial Intelligence and Law (Melbourne, Australia) (ICAIL '97). Association for Computing Machinery, New York, NY, USA, 151--160. https://doi.org/10.1145/261618.261648
[29]
Douglas Walton, G. Sartor, and F. Macagno. 2016. Contested Cases of Statutory Interpretation. Artificial Intelligence and Law 1, 24 (2016), 51--91.
[30]
Hannes Westermann, Jaromír Savelka, Vern R. Walker, Kevin D. Ashley, and Karim Benyekhlef. 2021. Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents. CoRR abs/2112.11494 (2021). arXiv:2112.11494 https://arxiv.org/abs/2112.11494
[31]
J. Wróblewski. 1992. The Judicial Application of Law. Kluwer Academic Publishers.
[32]
T Zurek and M Araszkiewicz. 2013. Modeling teleological interpretation. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law. ACM, 160--168.
Index Terms
- Identification of Legislative Errors
Recommendations
Identification of Legislative Errors Through Knowledge Representation and Interpretive Argumentation
AI Approaches to the Complexity of Legal Systems XI-XIIAbstractHigh-quality legislative drafting requires not only linguistic fluency, but also in-depth legal expertise. In this paper we focus on the second problem and we develop a frame-based, semi-formal model useful in identification and discussion of ...
Modeling Cybercrime with UFO: An Ontological Analysis of Non-Consensual Pornography Cases
Conceptual ModelingAbstractThe legal domain has challenging aspects. One is that legislation is written to regulate an unpredictable number of cases and, therefore, the language of the law is made of open texture and ambiguous terms. Another peculiarity is that each country ...
DocLog: An Electronic Contract Representation Language
DEXA '00: Proceedings of the 11th International Workshop on Database and Expert Systems ApplicationsThe legal aspects of electronic commerce have attracted widespread attention. Many organisations have expressed their concern regarding the legal framework of the Internet and electronic commerce in particular. For small and medium sized enterprises (...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2023
499 pages
ISBN:9798400701979
DOI:10.1145/3594536
- Conference Chair:
- Francisco Andrade,
- Program Chair:
- Matthias Grabmair
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
- IAAIL: Intl Asso for Artifical Intel & Law
In-Cooperation
- UMinho: University of Minho
- SIGAI: ACM Special Interest Group on Artificial Intelligence
- AAAI: Am Assoc for Artifical Intelligence
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 September 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICAIL 2023
Sponsor:
- IAAIL
ICAIL 2023: Nineteenth International Conference on Artificial Intelligence and Law
June 19 - 23, 2023
Braga, Portugal
Acceptance Rates
Overall Acceptance Rate 69 of 169 submissions, 41%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 46Total Downloads
- Downloads (Last 12 months)46
- Downloads (Last 6 weeks)3
Reflects downloads up to 29 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in