How to justify a backing’s eligibility for a warrant: the justification of a legal interpretation in a hard case

IF 3.1 2区 社会学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence and Law Pub Date : 2022-03-25 DOI:10.1007/s10506-022-09311-0
Shiyang Yu, Xi Chen
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引用次数: 1

Abstract

The Toulmin model has been proved useful in law and argumentation theory. This model describes the basic process in justifying a claim, which comprises six elements, i.e., claim (C), data (D), warrant (W), backing (B), qualifier (Q), and rebuttal (R). Specifically, in justifying a claim, one must put forward ‘data’ and a ‘warrant’, whereas the latter is authorized by ‘backing’. The force of the ‘claim’ being justified is represented by the ‘qualifier’, and the condition under which the claim cannot be justified is represented as the ‘rebuttal’. To further improve the model, (Goodnight, Informal Logic 15:41–52, 1993) points out that the selection of a backing needs justification, which he calls legitimation justification. However, how such justification is constituted has not yet been clarified. To identify legitimation justification, we separate it into two parts. One justifies a backing’s eligibility (legitimation justification1; LJ1); the other justifies its superiority over other eligible backings (legitimation justification2; LJ2). In this paper, we focus on LJ1 and apply it to the legal justification (of judgements) in hard cases for illustration purposes. We submit that LJ1 refers to the justification of the legal interpretation of a norm by its backing, which can be further separated into several orderable subjustifications. Taking the subjustification of a norm’s existence as an example, we show how it would be influenced by different positions in the philosophy of law. Taking the position of the theory of natural law, such subjustification is presented and evaluated. This paper aims not only to inform ongoing theoretical efforts to apply the Toulmin model in the legal field, but it also seeks to clarify the process in the justification of legal judgments in hard cases. It also offers background information for the possible construction of related AI systems. In our future work, LJ2 and other subjustifications of LJ1 will be discussed.

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如何证明持证人有资格获得搜查令:在一个棘手的案件中证明法律解释的正当性
图尔明模型在法律和论证理论中已被证明是有用的。该模型描述了证明索赔的基本过程,包括六个要素,即索赔(C)、数据(D)、保证(W)、支持(B)、限定符(Q)和反驳(R)。具体而言,在证明索赔的正当性时,必须提出“数据”和“搜查令”,而后者则得到“支持”的授权。证明“索赔”正当的效力由“限定词”表示,不能证明索赔正当的条件由“反驳”表示。为了进一步改进该模型,(Goodnight,《非正式逻辑》15:41-521993)指出,选择支持需要正当性,他称之为合法性正当性。然而,这种理由是如何构成的,目前尚不清楚。为了确定正当性辩护,我们将其分为两部分。一个证明支持的资格(合法化证明1;LJ1);另一个证明其优于其他合格的背景(合法化证明2;LJ2)。在本文中,我们关注LJ1,并将其应用于疑难案件中的(判决的)法律辩护,以便于说明。我们认为,LJ1指的是通过其支持对规范进行法律解释的正当性,可以进一步分为几个可排序的子正当性。以规范存在的次正当性为例,我们展示了它将如何受到法哲学中不同立场的影响。从自然法理论的立场出发,提出并评价了这种次论证。本文不仅旨在为将图尔明模型应用于法律领域的理论努力提供信息,而且还试图澄清在疑难案件中法律判决的正当性过程。它还为可能构建的相关人工智能系统提供了背景信息。在我们未来的工作中,将讨论LJ2和LJ1的其他子理由。
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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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