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Designing and Understanding Forensic Bayesian Networks with Arguments and Scenarios

Language: NL Lucia de Berk heeft het ondervonden: bewijs op basis van statistiek leidt gemakkelijk tot fouten. Dit project beoogt het maken van zulke fouten te helpen voorkomen. De nieuwe aanpak van het project is om de succesvolle statistische modelleertechniek van Bayesiaanse netwerken te koppelen aan goed bij de juridische denkwereld aansluitende modellen van argumentatie en scenarioconstructie.

Language: EN Lucia de Berk found out first-hand: evidence based on statistics can easily lead to errors. This project aims to help prevent this sort of error from occurring. The project's new approach is to link the successful statistical modelling technique of Bayesian networks to models that effectively dovetail legal argumentation and scenario construction in the legal world.

Scientific summary

Recent miscarriages of justice have increased the interest from legal practice in scientifically founded ways of treating evidence. Forensic statistics can provide such foundations. However, because of the communication gap between forensic statisticians, crime investigators and lawyers, statistical evidence is easily misinterpreted in court, resulting in wrong decisions. Therefore, methods must be developed to support the communication between the parties involved. Since lawyers are more used to thinking in terms of arguments and scenarios, we propose to develop methods that support argumentation- and narrative-based communication about statistical evidence, building on AI models of argumentation and scenario construction.

We focus on Bayesian Networks, since their graphical structure can be used to express scenarios, while they also support probabilistic reasoning. To draw inferences from Bayesian Networks, lawyers must understand how the evidence was modelled and what the model means. Therefore, support tools will be developed both for the modelling of evidence as a Bayesian Network and for the understanding of the resulting network. To support the modelling of evidence in Bayesian Networks, argumentation tools will be developed to model inferences and disagreement between experts, while narrative tools will be developed to support the construction of alternative scenarios. To aid the understanding of a Bayesian Network, tools will be developed for automatically extracting both arguments and scenarios from a network and for comparing alternative scenarios. The mathematical and computational tools developed in the project will be practically assessed by means of realistic case studies and training sessions in collaboration with forensic legal practice.

Team members

  • Dr. B. Verheij, principal investigator (Department of Artificial Intelligence, University of Groningen)
  • Prof. dr. mr. H. Prakken (Faculty of Law, University of Groningen, Department of Information and Computing Sciences, Utrecht University)
  • Dr. S. Renooij (Department of Information and Computing Sciences, Utrecht University)

  • Sjoerd Timmer, PhD Researcher 1 (Department of Information and Computing Sciences, Utrecht University)
  • Charlotte Vlek, PhD Researcher 2 (Department of Artificial Intelligence, University of Groningen)

  • Prof. dr. J.-J. Ch. Meyer (Department of Information and Computing Sciences, Utrecht University)
  • Prof. dr. L.C. Verbrugge (Department of Artificial Intelligence, University of Groningen)

  • Dr. F.J. Bex (Argumentation Research Group, School of Computing, University of Dundee)

  • Mr. G.C. Haverkate (Wetenschappelijk Bureau Openbaar Ministerie, WBOM)
  • Mr. A. Bood (Wetenschappelijk Bureau Openbaar Ministerie, WBOM)

  • Mr. drs. J. Moors (district court Amsterdam, Opleidingsinstituut van de Rechterlijke Organisatie, SSR)
  • Mr. H.W.G. Stikkelbroeck (court of appeal Arnhem, Opleidingsinstituut van de Rechterlijke Organisatie, SSR)
  • B.G.L. Stinissen (Opleidingsinstituut van de Rechterlijke Organisatie, SSR)
  • Mr. E.J. Willekers (Opleidingsinstituut van de Rechterlijke Organisatie, SSR)


Program web site: NWO Forensic Science
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