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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.

Publications

Dissertations

  1. Vlek, C. (2016). When Stories and Numbers Meet in Court. Constructing and Explaining Bayesian Networks for Criminal Cases with Scenarios. October 28, 2016. Dissertation University of Groningen.
  2. Timmer, S. (2017). Designing and Understanding Forensic Bayesian Networks using Argumentation. February 1, 2017. Dissertation Utrecht University.

Journal articles

  1. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2014). Building Bayesian Networks for Legal Evidence with Narratives: a Case Study Evaluation. Artificial Intelligence and Law 22 (4), 375-421. http://dx.doi.org/10.1007/s10506-014-9161-7 details  pdf  doi
  2. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2016). A Method for Explaining Bayesian Networks for Legal Evidence with Scenarios. Artificial Intelligence and Law 24 (3), 285-324. http://dx.doi.org/10.1007/s10506-016-9183-4 details  pdf  doi
  3. Verheij, B., Bex, F.J., Timmer, S., Vlek, C., Meyer, J.J., Renooij, S., & Prakken, H. (2016). Arguments, Scenarios and Probabilities: Connections Between Three Normative Frameworks for Evidential Reasoning. Law, Probability & Risk 15, 35-70. http://dx.doi.org/10.1093/lpr/mgv013 details  pdf  doi
  4. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2017). A Two-phase Method for Extracting Explanatory Arguments from Bayesian Networks. International Journal of Approximate Reasoning 80, 475-494. http://dx.doi.org/10.1016/j.ijar.2016.09.002 details  pdf  doi

Conference contributions (peer reviewed)

  1. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2013). Modeling Crime Scenarios in a Bayesian Network. The 14th International Conference on Artificial Intelligence and Law (ICAIL 2013). Proceedings of the Conference, 150-159. New York (New York): ACM. details  pdf
  2. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2013). Representing and Evaluating Legal Narratives with Subscenarios in a Bayesian Network. Proceedings of the 2013 Workshop on Computational Models of Narrative (CMN 2013) (eds. Finlayson, M.A., Fisseni, B., Löwe, B., & Meister, J.C.), 315-332. Dagstuhl: OASICS. details  pdf
  3. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2013). Inference and Attack in Bayesian Networks. 25th Benelux Conference on Artificial Intelligence (BNAIC 2013) (eds. Hindriks, K., De Weerdt, M., Van Riemsdijk, B., & Warnier, M.), 199-206. Delft: Delft University. details  pdf
  4. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2013). Unfolding Crime Scenarios with Variations: A Method for Building a Bayesian Network for Legal Narratives. Legal Knowledge and Information Systems. JURIX 2013: The Twenty-Sixth Annual Conference (ed. Ashley, K.D.), 145-154. Amsterdam: IOS Press. details  pdf
  5. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2014). Extracting Legal Arguments from Forensic Bayesian Networks. Legal Knowledge and Information Systems. JURIX 2014: The Twenty-Seventh Annual Conference (ed. Hoekstra, R.), 71-80. Amsterdam: IOS Press. details  pdf
  6. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2014). Extracting Scenarios from a Bayesian Network as Explanations for Legal Evidence. Legal Knowledge and Information Systems. JURIX 2014: The Twenty-Seventh Annual Conference (ed. Hoekstra, R.), 103-112. Amsterdam: IOS Press. details  pdf
  7. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2015). Constructing and Understanding Bayesian Networks for Legal Evidence with Scenario Schemes. The 15th International Conference on Artificial Intelligence and Law (ICAIL 2015). Proceedings of the Conference, 128-137. New York (New York): ACM. http://dx.doi.org/10.1145/2746090.2746097 details  pdf  doi
  8. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). A Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. The 15th International Conference on Artificial Intelligence and Law (ICAIL 2015). Proceedings of the Conference, 109-118. New York (New York): ACM. http://dx.doi.org/10.1145/2746090.2746093 details  pdf  doi
  9. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining Legal Bayesian networks Using Support Graphs. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (ed. Rotolo, A.), 121-130. Amsterdam: IOS Press. http://dx.doi.org/10.3233/978-1-61499-609-5-121 details  pdf  doi
  10. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Capturing Critical Questions in Bayesian Network Fragments. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (ed. Rotolo, A.), 173-176. Amsterdam: IOS Press. http://dx.doi.org/10.3233/978-1-61499-609-5-173 details  pdf  doi
  11. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Explaining Bayesian Networks using Argumentation. Symbolic and Quantitative Approaches to Reasoning with Uncertainty 13th European Conference, ECSQARU 2015, Compiègne, France, July 15-17, 2015. Proceedings (eds. Destercke, S., & Denoeux, T.), 83-92. Berlin: Springer. http://dx.doi.org/10.1007/978-3-319-20807-7_8 details  pdf  doi
  12. Vlek, C., Prakken, H., Renooij, S., & Verheij, B. (2015). Representing the Quality of Crime Scenarios in a Bayesian Network. Legal Knowledge and Information Systems. JURIX 2015: The Twenty-eighth Annual Conference (ed. Rotolo, A.), 131-140. Amsterdam: IOS Press. http://dx.doi.org/10.3233/978-1-61499-609-5-131 details  pdf  doi

Other publications (demonstrations, abstracts)

  1. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2014). A Tool for the Generation of Arguments from Bayesian Networks. Computational Models of Argument. Proceedings of COMMA 2014 (eds. Parsons, S., Oren, N., Reed, C., & Cerutti, F.), 479-480. Amsterdam: IOS Press. http://dx.doi.org/10.3233/978-1-61499-436-7-479 details  pdf  doi
  2. Timmer, S., Meyer, J.J., Prakken, H., Renooij, S., & Verheij, B. (2015). Demonstration of a Structure-guided Approach to Capturing Bayesian Reasoning about Legal Evidence in Argumentation. The 15th International Conference on Artificial Intelligence and Law (ICAIL 2015). Proceedings of the Conference, 233-234. New York (New York): ACM. http://dx.doi.org/10.1145/2746090.2750370 details  pdf  doi
  3. Verheij, B., Bex, F.J., Timmer, S., Vlek, C., Meyer, J.J., Renooij, S., & Prakken, H. (2016). Arguments, Scenarios and Probabilities: Connections Between Three Normative Frameworks for Evidential Reasoning (Abstract). Proceedings of the 28th Benelux Conference on Artificial Intelligence (BNAIC 2016) (eds. Bredeweg, B., & Bosse, T.), 192-193. details  pdf


Team members

  • Prof. 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 		News