This paper describes an approach to legal logic based on the formal analysis of argumentation schemes. Argumentation schemes a notion borrowed from the .eld of argumentation theory - are a kind of generalized rules of inference, in the sense that they express that given certain premises a particular conclusion can be drawn. However, argumentation schemes need not concern strict, abstract, necessarily valid patterns of reasoning, but can be defeasible, concrete and contingently valid, i.e., valid in certain contexts or under certain (...) circumstances. A method is presented to analyze argumentation schemes and it is shown how argumentation schemes can be embedded in a formal model of dialectical argumentation. (shrink)
Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a (...) causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock’s film ‘To Catch A Thief’. (shrink)
Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)
In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing (...) parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study. (shrink)
This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of (...) an event, e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper, we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence. Therefore, a hybrid approach is proposed and formally developed, doing justice to both the argument-based and the narrative-based perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for the design of software developed as a tool to make sense of the evidence in complex cases. (shrink)
We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the development of AI and Law, while often offering some personal reactions and reflections. As a whole, the subsections build into (...) a history of the last quarter century of the field, and provide some insights into where it has come from, where it is now, and where it might go. (shrink)
In The Uses of Argument, Stephen Toulmin proposed a model for the layout of arguments: claim, data, warrant, qualifier, rebuttal, backing. Since then, Toulmin’s model has been appropriated, adapted and extended by researchers in speech communications, philosophy and artificial intelligence. This book assembles the best contemporary reflection in these fields, extending or challenging Toulmin’s ideas in ways that make fresh contributions to the theory of analysing and evaluating arguments.
Doug Walton, who died in January 2020, was a prolific author whose work in informal logic and argumentation had a profound influence on Artificial Intelligence, including Artificial Intelligence and Law. He was also very interested in interdisciplinary work, and a frequent and generous collaborator. In this paper seven leading researchers in AI and Law, all past programme chairs of the International Conference on AI and Law who have worked with him, describe his influence on their work.
Toulmin’s scheme for the layout of arguments (1958, The Uses of Argument, Cambridge University Press, Cambridge) represents an influential tool for the analysis of arguments. The scheme enriches the traditional premises-conclusion model of arguments by distinguishing additional elements, like warrant, backing and rebuttal. The present paper contains a formal elaboration of Toulmin’s scheme, and extends it with a treatment of the formal evaluation of Toulmin-style arguments, which Toulmin did not discuss at all. Arguments are evaluated in terms of a so-called (...) dialectical interpretation of their assumptions. In such an interpretation, an argument’s assumptions can be evaluated as defeated, e.g., when there is a defeating reason against the assumption. The present work builds on recent research on defeasible argumentation (cf. e.g. the work of Pollock, Reiter, Loui, Vreeswijk, Prakken, Hage and Dung). More specifically, the author’s work on the dialectical logic DEFLOG and the argumentation tool ARGUMED serve as starting points. (shrink)
This paper is one in a series of rational analyses of the Dutch Simonshaven case, each using a different theoretical perspective. The theoretical perspectives discussed in the literature typically use arguments, scenarios, and probabilities, in various combinations. The theoretical perspective on evidential reasoning used in this paper has been designed to connect arguments, scenarios, and probabilities in a single formal modeling approach, in an attempt to investigate bridges between qualitative and quantitative analytic styles. The theoretical perspective uses the recently proposed (...) logical formalism of case models, where cases represent possible combinations of evidence and events, ordered by an ordering relation. In the context of evidential reasoning, the ordering relation can be represented by a probability function. As an ordering relation is qualitative in nature, the theoretical perspective is in a formally precise sense simultaneously with and without probabilities. (shrink)
In this paper, we look at reasoning with evidence and facts in criminal cases. We show how this reasoning may be analysed in a dialectical way by means of critical questions that point to typical sources of doubt. We discuss critical questions about the evidential arguments adduced, about the narrative accounts of the facts considered, and about the way in which the arguments and narratives are connected in an analysis. Our treatment shows how two different types of knowledge, represented as (...) schemes, play a role in reasoning with evidence: argumentation schemes and story schemes. (shrink)
In a criminal trial, evidence is used to draw conclusions about what happened concerning a supposed crime. Traditionally, the three main approaches to modeling reasoning with evidence are argumentative, narrative and probabilistic approaches. Integrating these three approaches could arguably enhance the communication between an expert and a judge or jury. In previous work, techniques were proposed to represent narratives in a Bayesian network and to use narratives as a basis for systematizing the construction of a Bayesian network for a legal (...) case. In this paper, these techniques are combined to form a design method for constructing a Bayesian network based on narratives. This design method is evaluated by means of an extensive case study concerning the notorious Dutch case of the Anjum murders. (shrink)
The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.
In this paper, we continue our research on a hybrid narrative-argumentative approach to evidential reasoning in the law by showing the interaction between factual reasoning (providing a proof for ‘what happened’ in a case) and legal reasoning (making a decision based on the proof). First we extend the hybrid theory by making the connection with reasoning towards legal consequences. We then emphasise the role of legal stories (as opposed to the factual stories of the hybrid theory). Legal stories provide a (...) coherent, holistic legal perspective on a case. They steer what needs to be proven but are also selected on the basis of what can be proven. We show how these legal stories can be used to model a shift of the legal perspective on a case, and we discuss how gaps in a legal story can be filled using a factual story (i.e. the process of reasoning with circumstantial evidence). Our model is illustrated by a discussion of the Dutch Wamel murder case. (shrink)
This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences.
The primary aim of this chapter is to explain the nature of evidential reasoning, the characteristic difficulties encountered, and the tools to address these difficulties. Our focus is on evidential reasoning in criminal cases. There is an extensive scholarly literature on these topics, and it is a secondary aim of the chapter to provide readers the means to find their way in historical and ongoing debates.
In the law, it is generally acknowledged that there are intuitive differences between reasoning with rules and reasoning with principles. For instance, a rule seems to lead directly to its conclusion if its condition is satisfied, while a principle seems to lead merely to a reason for its conclusion. However, the implications of these intuitive differences for the logical status of rules and principles remain controversial.A radical opinion has been put forward by Dworkin (1978). The intuitive differences led him to (...) argue for a strict logical distinction between rules and principles. Ever since, there has been a controversy whether the intuitive differences between rules and principles require a strict logical distinction between the two. For instance, Soeteman (1991) disagrees with Dworkin's opinion, and argues that rules and principles cannot be strictly distinguished, and do not have a different logical structure. (shrink)
This handbook offers a deep analysis of the main forms of legal reasoning and argumentation from both a logical-philosophical and legal perspective. These forms are covered in an exhaustive and critical fashion, and the handbook accordingly divides in three parts: the first one introduces and discusses the basic concepts of practical reasoning. The second one discusses the main general forms of reasoning and argumentation relevant for legal discourse. The third one looks at their application in law as well as at (...) the different areas of legal reasoning. The handbook’s division in three parts reflects its conceptual architecture, since legal reasoning and argumentation are considered in relation to the more general types of reasoning. (shrink)
This paper provides a formal description of two legal domains. In addition, we describe the generation of various artificial datasets from these domains and explain the use of these datasets in previous experiments aligning learning and reasoning. These resources are made available for the further investigation of connections between arguments, cases and rules. The datasets are publicly available at https://github.com/CorSteging/LegalResources.
dialectical frameworks have been introduced as a formalism for modeling argumentation allowing general logical satisfaction conditions and the relevant argument evaluation. Different criteria used to settle the acceptance of arguments are called semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. However, the notion of strongly admissible semantics studied for abstract argumentation frameworks has not yet been introduced for ADFs. In the current work we present the concept of strong admissibility of interpretations for (...) ADFs. Further, we show that strongly admissible interpretations of ADFs form a lattice with the grounded interpretation as the maximal element. We also present algorithms to answer the following decision problems: whether a given interpretation is a strongly admissible interpretation of a given ADF, and whether a given argument is strongly acceptable/deniable in a given interpretation of a given ADF. In addition, we show that the strongly admissible semantics of ADFs forms a proper generalization of the strongly admissible semantics of AFs. (shrink)