Encoding legislation: a methodology for enhancing technical validation, legal alignment and interdisciplinarity

IF 3.1 2区 社会学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Alice Witt, Anna Huggins, Guido Governatori, Joshua Buckley
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引用次数: 0

Abstract

This article proposes an innovative methodology for enhancing the technical validation, legal alignment and interdisciplinarity of attempts to encode legislation. In the context of an experiment that examines how different legally trained participants convert select provisions of the Australian Copyright Act 1968 (Cth) into machine-executable code, we find that a combination of manual and automated methods for coding validation, which focus on formal adherence to programming languages and conventions, can significantly increase the similarity of encoded rules between coders. Participants nonetheless encountered various interpretive difficulties, including syntactic ambiguity, and intra- and intertextuality, which necessitated legal evaluation, as distinct from and in addition to coding validation. Many of these difficulties can be resolved through what we call a process of ‘legal alignment’ that aims to enhance the congruence between encoded provisions and the true meaning of a statute as determined by the courts. However, some difficulties cannot be overcome in advance, such as factual indeterminacy. Given the inherently interdisciplinary nature of encoding legislation, we argue that it is desirable for ‘rules as code’ (‘RaC’) initiatives to have, at a minimum, legal subject matter, statutory interpretation and technical programming expertise. Overall, we contend that technical validation, legal alignment and interdisciplinary teamwork are integral to the success of attempts to encode legislation. While legal alignment processes will vary depending on jurisdictionally-specific principles and practices of statutory interpretation, the technical and interdisciplinary components of our methodology are transferable across regulatory contexts, bodies of law and Commonwealth and other jurisdictions.

编码立法:一种加强技术验证、法律一致性和跨学科性的方法
本文提出了一种创新方法,用于加强立法编码尝试的技术验证、法律协调和跨学科性。在一项研究不同受过法律培训的参与者如何将《1968 年澳大利亚版权法》(Cth)中的部分条款转换为机器可执行代码的实验中,我们发现,手动和自动编码验证方法的结合(侧重于正式遵守编程语言和惯例)可以显著提高编码者之间编码规则的相似性。尽管如此,参与者还是遇到了各种解释上的困难,包括句法歧义、文本内和文本间性,这就需要进行法律评估,有别于编码验证,也有别于编码验证。其中许多困难可以通过我们所说的 "法律调整 "过程来解决,该过程旨在加强编码条款与法院确定的法规真正含义之间的一致性。然而,有些困难是无法事先克服的,例如事实的不确定性。鉴于立法编码本身具有跨学科性质,我们认为 "规则即代码"("RaC")计划至少应具备法律主题、法规解释和技术编程方面的专业知识。总之,我们认为技术验证、法律协调和跨学科团队合作是立法编码成功不可或缺的因素。虽然法律协调过程会因特定司法管辖区的法定解释原则和实践而有所不同,但我们方法中的技术和跨学科组成部分可在不同的监管环境、法律机构以及英联邦和其他司法管辖区之间进行转换。
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来源期刊
CiteScore
9.50
自引率
26.80%
发文量
33
期刊介绍: Artificial Intelligence and Law is an international forum for the dissemination of original interdisciplinary research in the following areas: Theoretical or empirical studies in artificial intelligence (AI), cognitive psychology, jurisprudence, linguistics, or philosophy which address the development of formal or computational models of legal knowledge, reasoning, and decision making. In-depth studies of innovative artificial intelligence systems that are being used in the legal domain. Studies which address the legal, ethical and social implications of the field of Artificial Intelligence and Law. Topics of interest include, but are not limited to, the following: Computational models of legal reasoning and decision making; judgmental reasoning, adversarial reasoning, case-based reasoning, deontic reasoning, and normative reasoning. Formal representation of legal knowledge: deontic notions, normative modalities, rights, factors, values, rules. Jurisprudential theories of legal reasoning. Specialized logics for law. Psychological and linguistic studies concerning legal reasoning. Legal expert systems; statutory systems, legal practice systems, predictive systems, and normative systems. AI and law support for legislative drafting, judicial decision-making, and public administration. Intelligent processing of legal documents; conceptual retrieval of cases and statutes, automatic text understanding, intelligent document assembly systems, hypertext, and semantic markup of legal documents. Intelligent processing of legal information on the World Wide Web, legal ontologies, automated intelligent legal agents, electronic legal institutions, computational models of legal texts. Ramifications for AI and Law in e-Commerce, automatic contracting and negotiation, digital rights management, and automated dispute resolution. Ramifications for AI and Law in e-governance, e-government, e-Democracy, and knowledge-based systems supporting public services, public dialogue and mediation. Intelligent computer-assisted instructional systems in law or ethics. Evaluation and auditing techniques for legal AI systems. Systemic problems in the construction and delivery of legal AI systems. Impact of AI on the law and legal institutions. Ethical issues concerning legal AI systems. In addition to original research contributions, the Journal will include a Book Review section, a series of Technology Reports describing existing and emerging products, applications and technologies, and a Research Notes section of occasional essays posing interesting and timely research challenges for the field of Artificial Intelligence and Law. Financial support for the Journal of Artificial Intelligence and Law is provided by the University of Pittsburgh School of Law.
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