Argument Schemes for Reasoning with Legal Cases Using Values

ABSTRACT Argument schemes can provide a means of explicitly describing reasoning methods in a form that lends itself to computation. The reasoning required to distinguish cases in the manner of CATO has been previously captured as a set of argument schemes. Here we present argument schemes that encapsulate another way of reasoning with cases: using preferences between social values revealed in past decisions to decide cases which have no exact matching precedents when the cases are described in terms of factors. We provide a set of schemes, with variations to capture different ways of comparing sets and varying degrees of promotion of values; we formalise these schemes; and we illustrate them with some examples

Modeling Purposive Legal Argumentation and Case Outcome Prediction using Argument Schemes�in the Value Judgment Formalism

Artificial Intelligence and Law studies how legal reasoning can be formalized in order to eventually be able to develop systems that assist lawyers in the task of researching, drafting and evaluating arguments in a professional setting. To further this goal, researchers have been developing systems, which, to a limited extent, autonomously engage in legal reasoning, and argumentation on closed domains. This dissertation presents the Value Judgment Formalism and its experimental implementation in the VJAP system, which is capable of arguing about, and predicting outcomes of, a set of trade secret misappropriation cases. VJAP argues about cases by creating an argument graph for each case using a set of argument schemes. These schemes use a representation of values underlying trade secret law and effects of facts on these values. VJAP argumentatively balances effects in the given case and analogizes it to individual precedents and the value tradeoffs in those precedents. It predicts case outcomes using a confidence measure computed from the argument graph and generates textual legal arguments justifying its predictions. The confidence propagation uses quantitative weights assigned to effects of facts on values. VJAP automatically learns these weights from past cases using an iterative optimization method. The experimental evaluation shows that VJAP generates case-based legal arguments that make plausible and intelligent-appearing use of precedents to reason about a case in terms of differences and similarities to a precedent and the value tradeoffs that both contain. VJAP’s prediction performance is promising when compared to machine learning algorithms, which do not generate legal arguments. Due to the small case base, however, the assessment of prediction performance was not statistically rigorous. VJAP exhibits argumentation and prediction behavior that, to some extent, resembles phenomena in real case-based legal reasoning, such as realistically appearing citation graphs. The VJAP system and experiment demonstrate that it is possible to effectively combine symbolic knowledge and inference with quantitative confidence propagation. In AI\&Law, such systems can embrace the structure of legal reasoning and learn quantitative information about the domain from prior cases, as well as apply this information in a structurally realistic way in the context of new cases

Subjective Logic and Arguing with Evidence

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After the Great Recession: Law and Economics\u27 Topics of Invention and Arrangement and Tropes of Style

AFTER THE GREAT RECESSION: LAW AND ECONOMICS’ TOPICS OF INVENTION AND ARRANGEMENT AND TROPES OF STYLE by Michael D. Murray Abstract The Great Recession of 2008 and onward has drawn attention to the American economic and financial system, and has cast a critical spotlight on the theories, policies, and assumptions of the modern, neoclassical school of law and economics—often labeled the Chicago School —because this school of legal economic thought has had great influence on the American economy and financial system. The Chicago School\u27s positions on deregulation and the limitation or elimination of oversight and government restraints on stock markets, derivative markets, and other financial practices are the result of decades of neoclassical economic assumptions regarding the efficiency of unregulated markets, the near-religious-like devotion to a hyper-simplified conception of rationality and self-interest with regard to the persons and institutions participating in the financial system, and a conception of laws and government policies as incentives and costs in a manner that excludes the actual conditions and complications of reality. This Article joins the critical conversation on the Great Recession and the role of law and economics in this crisis by examining neoclassical and contemporary law and economics from the perspective of legal rhetoric. Law and economics has developed into a school of contemporary legal rhetoric that provides topics of invention and arrangement and tropes of style to test and improve general legal discourse in areas beyond the economic analysis of law. The rhetorical canons of law and economics—mathematical and scientific methods of analysis and demonstration; the characterization of legal phenomena as incentives and costs; the rhetorical economic concept of efficiency; and rational choice theory as corrected by modern behavioral social sciences, cognitive studies, and brain science—make law and economics a persuasive method of legal analysis and a powerful school of contemporary legal rhetoric, if used in the right hands. My Article is the first to examine the prescriptive implications of the rhetoric of law and economics for general legal discourse as opposed to examining the benefits and limitations of the economic analysis of law itself. This Article advances the conversation in two areas: first, as to the study and understanding of the persuasiveness of law and economics, particularly because that persuasiveness has played a role in influencing American economic and financial policy leading up to the Great Recession; and second, as to the study and understanding of the use of economic topics of invention and arrangement and tropes of style in general legal discourse when evaluated in comparison to the other schools of classical and contemporary legal rhetoric. I examine each of the rhetorical canons of law and economics and explain how each can be used to create meaning, inspire imagination, and improve the persuasiveness of legal discourse in every area of law. My conclusion is that the rhetorical canons of law and economics can be used to create meaning and inspire imagination in legal discourse beyond the economic analysis of law, but the canons are tools that only are as good as the user, and can be corrupted in ways that helped to bring about the current economic crisis

Explanation for case-based reasoning via abstract argumentation

Case-based reasoning (CBR) is extensively used in AI in support of several applications, to assess a new situation (or case) by recollecting past situations (or cases) and employing the ones most similar to the new situation to give the assessment. In this paper we study properties of a recently proposed method for CBR, based on instantiated Abstract Argumentation and referred to as AA-CBR, for problems where cases are represented by abstract factors and (positive or negative) outcomes, and an outcome for a new case, represented by abstract factors, needs to be established. In addition, we study properties of explanations in AA-CBR and define a new notion of lean explanations that utilize solely relevant cases. Both forms of explanations can be seen as dialogical processes between a proponent and an opponent, with the burden of proof falling on the proponent

Argumentation Mining in User-Generated Web Discourse

The goal of argumentation mining, an evolving research field in computational linguistics, is to design methods capable of analyzing people's argumentation. In this article, we go beyond the state of the art in several ways. (i) We deal with actual Web data and take up the challenges given by the variety of registers, multiple domains, and unrestricted noisy user-generated Web discourse. (ii) We bridge the gap between normative argumentation theories and argumentation phenomena encountered in actual data by adapting an argumentation model tested in an extensive annotation study. (iii) We create a new gold standard corpus (90k tokens in 340 documents) and experiment with several machine learning methods to identify argument components. We offer the data, source codes, and annotation guidelines to the community under free licenses. Our findings show that argumentation mining in user-generated Web discourse is a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17

The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants

Reasoning is a crucial part of natural language argumentation. To comprehend an argument, one must analyze its warrant, which explains why its claim follows from its premises. As arguments are highly contextualized, warrants are usually presupposed and left implicit. Thus, the comprehension does not only require language understanding and logic skills, but also depends on common sense. In this paper we develop a methodology for reconstructing warrants systematically. We operationalize it in a scalable crowdsourcing process, resulting in a freely licensed dataset with warrants for 2k authentic arguments from news comments. On this basis, we present a new challenging task, the argument reasoning comprehension task. Given an argument with a claim and a premise, the goal is to choose the correct implicit warrant from two options. Both warrants are plausible and lexically close, but lead to contradicting claims. A solution to this task will define a substantial step towards automatic warrant reconstruction. However, experiments with several neural attention and language models reveal that current approaches do not suffice.Comment: Accepted as NAACL 2018 Long Paper; see details on the front pag