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|id=Vol-2632/mirel_2019_introduction
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2632/mirel_2019_introduction.pdf
|volume=Vol-2632
|authors=Leendert van der Torre
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==None==
https://ceur-ws.org/Vol-2632/mirel_2019_introduction.pdf
Mining and Reasoning with Legal Texts
(MIREL)
Horizon 2020 Marie Sklodowska-Curie RISE Project
2016 - 2019
Leendert van der Torre
Computer Science and Communications Research Unit, University of Luxembourg,
Luxembourg
Email: leon.vandertorre@uni.lu
Legal texts are stored in legal document management systems to help legal
professionals to retrieve the information they are interested in. Legal mining
and legal reasoning are examples of AI techniques used to automate repetitive
operations such as classifying, indexing, and discovering relations among legal
texts.
Legal mining is the application of natural language processing methods to
legal texts, in order to extract legal data and classify legal texts to facilitate
navigation and search. Examples of tools used in legal mining are parsers,
statistical algorithms and semantic knowledge bases.
Legal reasoning is the application of knowledge representation and reasoning
techniques to infer new knowledge and actions from legal data, for example
to support and monitor compliance assessment, or risk analysis.
To bridge the communities studying legal mining and legal reasoning, which
previously worked mostly in isolation, the H2020 project “MIREL - MIning and
Reasoning with Legal texts” focused on semantic aspects of law. Legal mining
is often done by transforming the source legal documents into XML standards,
where relevant information is tagged. The XML files are archived and queried
in subsequent phases. Although these techniques provide valid solutions to help
navigate legislation and retrieve information, the overall usefulness and effective-
ness of the systems are limited due to their focus on terminological issues and
information retrieval. The semantic aspects of law studied in the MIREL project
focus on its logical structure in terms of constitutive and regulative rules, thus
allowing legal reasoning.
The major challenge to bridge mining and reasoning in the MIREL project
is legal ambiguity and the related handling of multiple legal interpretations of the
provisions.
Legal interpretations are the context-specific pragmatic interpretations of
the terms and sentences occurring in legal texts.
Copyright c 2020 for this paper by its author. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
Legal ambiguity is not a bug, but a feature of legal reasoning. Since it is impossi-
ble to predict every possible context where the provisions will be used, legislators
use terms accounting for the multitude of situations that should be covered by
the legislation. Legal interpretation often depends on the precedent cases, and
on reflections of legal doctrine. It is eventually up to judges and other appointed
authorities to decide the legal interpretation of provisions in a specific context.
In borderline cases, judges may adopt distinct legal interpretations, incompati-
ble among themselves. Moreover, in legal interpretations in disputes may reflect
different interests of the legal stakeholders. Handling multiple interpretations
introduces an additional layer of complexity for legal reasoning.
Legal ontologies are the main instrument to address legal interpretations, and
thus to bridge mining and reasoning from legal texts. They define a set of legal
concepts and categories of the domain of legal discourse.
Legal ontologies are a representation, formal naming and definition of the
categories, properties and relations between the legal concepts, data and
entities.
Legal ontologies help legal practitioners and scholars to keep up to date with
continuous changes in the law and understand legal sub-languages outside their
own areas of expertise or jurisdiction, and legislators to draft legislation with
clarity and consistency. Moreover, they help identify the inter-relationship be-
tween general jurisdictions and specific related ones, e.g., between the jurisdiction
of the European Union and the ones of the Member States, in order to foster
harmonisation.
The increasing use of AI techniques such as mining and reasoning in legal
expert systems is studied in the field of AI and law. For example, Robaldo and
van der Torre [1] define the emerging field of Legal AI as follows:
Legal AI is the research area concerned with the AI-driven processing of norms
occurring in legislation and related documents like jurisprudence, interna-
tional standards, and doctrine in order to achieve compliance of the systems
with the regulations in force.
Moreover, they define compliance as follows:
Compliance checking in computer systems is the process of ensuring that the
specification requirements of such systems are in accordance with prescribed
and/or agreed compliance requirements. These norms may stem from legis-
lation and regulatory bodies (e.g., Sarbanes-Oxley, Basel II, HIPAA), stan-
dards and codes of practice (e.g., SCOR, ISO9000), and business partner
contracts.
A further increase of research in legal AI may be expected in the near future.
The European Union (EU) chose as one of its primary objectives to establish an
integrated and standardised system of laws that applies in all member states.
Legal AI has recently received a lot of investments from industry and institutions,
due to the 2008 global financial crisis and the connected rise of RegTech and
FinTech. As the law gets more complex, conflicting, and ever changing, more
advanced methodologies are required for analysing, representing and reasoning
on legal knowledge.
References
1. Livio Robaldo and Leendert W. N. van der Torre. Introduction to legal AI. Journal
of Applied Logics, 6(5):711–714, 2019.