09:30 to 10:15 Bart Verheij (University of Groningen)Three Normative Frameworks for Evidential Reasoning and their Connections: Arguments, Scenarios and Probabilities Artificial intelligence research on reasoning with criminal evidence in terms of arguments, hypothetical scenarios, and probabilities showed that Bayesian networks can be used for modeling arguments and structured hypotheses. Also 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 models arguments and scenarios in terms of models that have a probabilistic interpretation, but do not represent a full distribution over the variables. INI 1 10:15 to 11:00 Floris Bex (Universiteit Utrecht)From Natural Language to Bayesian Networks (and back again) Decision makers and analysts often use informal, linguistic concepts when they talk about a case: they tell the story that explains the evidence, or argue against a particular interpretation of the evidence. On the other hand, mathematicians and logicians present formal frameworks to precisely capture and support reasoning about evidence. In this talk, I will show how different Artificial Intelligence techniques can be used to close the gap between these two extremes - messy, informal natural language and specific, well-defined formalisms such as Bayesian Networks. INI 1 11:00 to 11:30 Morning Coffee 11:30 to 12:15 Martin Neil (Queen Mary University of London)Modelling competing Legal Arguments using Bayesian Model Comparison and Averaging Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment and in a way that makes sense with respect to the competing argument narratives. In contrast to this integrated approach, Non-Bayesian approaches to legal argumentation have tended to be narrative based and have focused on comparisons between competing stories and explanations. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach is consistent with subjectivist Bayesian philosophy. Practically, competing models of legal arguments are assessed by the extent to which the credibility of the sources of evidence are confirmed or disconfirmed in court. Those models that are more heavily disconfirmed are assigned lower weights, as model confidence measures, in the Bayesian model comparison and averaging approach adopted. In this way plurality of arguments are allowed yet a single judgement based on all arguments is possible and rational.Authors: Prof. Martin Neil (Queen Mary, University of London), Prof. Norman Fenton (Queen Mary, University of London), Prof David Lagnado (UCL), and Prof Richard Gill (Leiden University) INI 1 12:15 to 13:30 Lunch @ INI 13:30 to 14:15 Jacob de Zoete (Universiteit van Amsterdam)Generating Bayesian networks in Forensic Science An example from crime linkage Co-author: Marjan Sjerps (Netherlands Forensic Institute) The likelihood ratio framework for evaluating evidence is becoming more common in forensic practice. As a result, the interest in Bayesian networks as a tool to analyse cases and performing computations has increased. However, constructing a Bayesian network from scratch for every situation that one encounters is too costly. Therefore, several researchers have proposed Bayesian networks that correspond with frequent problems [1,2]. These building blocks' allow the user to only concentrate on the conditional probabilities that fit their particular situation. This results in a more efficient workflow: the effort to construct the Bayesian network is taken away. Furthermore, it is no longer necessary that the user is experienced in constructing Bayesian networks. However, when the problem does not follow the exact' assumptions of the building block, the Bayesian network can only serve as a starting point when constructing a model that does. In some situations, it is clear how one should model a certain problem, regardless of the case specific details. For example, a Bayesian network for a source level hypotheses pair where the evidence consists of a DNA profile has the same structure for any number of loci. Each locus can be added as a node together with it's corresponding drop-out/drop-in probabilities. For these type of problems, one can take away the effort of constructing the network. This facilitates the practical application of Bayesian networks for forensic casework. We will show an example of `generating Bayesian networks' for a problem from crime linkage. In [3] a structure for modeling crime linkage with Bayesian networks is introduced. This structure is implemented in R which allows the user to insert the parameters corresponding to their situation (e.g. the number of crimes/number of different types of evidence). Subsequently, this network can be used to obtain posterior probabilities or likelihood ratios. We will show how this is useful in casework. INI 1 14:15 to 15:00 Marjan Sjerps (Universiteit van Amsterdam)DNA myth busting with Bayesian networks co-authors J de Koeijer, J de Zoete and B Kokshoorn The Netherlands Forensic Institute is currently exploring the use of Bayesian networks in their forensic casework. We identified a number of different ways that networks can be used, e.g., as a probability calculator, as an exploratory tool for complex problems and as a reasoning tool. In this presentation we focus on the latter and discuss a recent case in which Bayesian networks were used in this way. The case concerned a series of six different robberies in which several DNA matches with a suspect were found on “movable objects” in each case. We were asked to assess the evidential value of the combined DNA evidence. Bayesian networks proved to be very valuable to assist in our reasoning and in busting a few important DNA myths that are common in the legal domain. We chose not to report the networks themselves but only the reasoning, and explain why. INI 1 15:00 to 15:30 Afternoon Tea 15:30 to 16:15 Maria Cuellar (Carnegie Mellon University)Shaken Baby Syndrome on Trial: Causal Problems and Sources of Bias Over 1,100 individuals are in prison today on charges related to the diagnosis of Shaken Baby Syndrome (SBS). In recent years this diagnosis has come under scrutiny, and more than 20 convictions made on the basis of SBS have been overturned. The overturned convictions have fueled a controversy about alleged cases of SBS. In this talk, I will review the arguments made by the prosecution and defense in cases related to SBS and point out two problems: much of the evidence used has contextual bias, and the expert witnesses and attorneys ask the wrong causal questions. To resolve the problem of asking the wrong causal questions, I suggest that a Causes of Effects framework be used in formulating the causal questions and answers given by attorneys and expert witnesses. To resolve the problem of bias, I suggest that only the task-relevant information be provided to the individual who determines the diagnosis. I also suggest that in order for this to be possible, there must be a change in the definition of SBS so it does not include the manner in which the injuries were caused. I close with recommendations to researchers in statistics and the law about how to use scientific results in court. INI 1 16:15 to 17:00 Bart Verheij (University of Groningen); Henry Prakken (Universiteit Utrecht); (University of Groningen)Report on a pre-workshop on analysing a murder case Floris Bex, Anne Ruth Mackor, Henry Prakken, Christian Dahlman, Martin Neil, Bart VerheijWe will discuss the results of a pre-workshop held at the INI on September 23-24. During this workshop a Dutch murder case was analyzed from several theoretical perspectives, including Bayesian, argumentative and narrative approaches. INI 1 19:30 to 22:00 Formal Dinner at Christ's College