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)
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)
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 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)
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)
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.
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)
Toulmin’s scheme for the layout of arguments 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 . More specifically, the author’s work on the dialectical logic DEFLOG and the argumentation tool ARGUMED serve as starting points. (shrink)
Evidential Reasoning.Marcello Di Bello & Bart Verheij - 2018 - In Colin Aitken, Amalia Amaya, Kevin D. Ashley, Carla Bagnoli, Giorgio Bongiovanni, Bartosz Brożek, Cristiano Castelfranchi, Samuele Chilovi, Marcello Di Bello, Jaap Hage, Kenneth Einar Himma, Lewis A. Kornhauser, Emiliano Lorini, Fabrizio Macagno, Andrei Marmor, J. J. Moreso, Veronica Rodriguez-Blanco, Antonino Rotolo, Giovanni Sartor, Burkhard Schafer, Chiara Valentini, Bart Verheij, Douglas Walton & Wojciech Załuski (eds.), Handbook of Legal Reasoning and Argumentation. Springer Verlag. pp. 447-493.details
When a suspect appears in front of a criminal court, there is a high probability that he will be found guilty. In the USA, statistics for recent years show that the conviction rate in federal courts is roughly 90%, and in Japan reaches as high a rate as 99%.
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.
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.
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)
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 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, and AI & Law inspires new solutions. 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)