Remi Wieten
ABSTRACT
Bayesian networks (BNs) are powerful tools that are increasingly being used by forensic and legal experts to reason about the uncertain conclusions that can be inferred from the evidence in a case. Although in BN construction it is good practice to document the model itself, the importance of documenting design decisions has received little attention. Such decisions, including the (possibly conflicting) reasons behind them, are important for legal experts to understand and accept probabilistic models of cases. Moreover, when disagreements arise between domain experts involved in the construction of BNs, there are no systematic means to resolve such disagreements. Therefore, we propose an approach that allows domain experts to explicitly express and capture their reasons pro and con modelling decisions using argumentation, and that resolves their disagreements as much as possible. Our approach is based on a case study, in which the argumentation structure of an actual disagreement between two forensic BN experts is analysed.
- N. Fenton and M. Neil. Risk Assessment and Decision Analysis with Bayesian Networks. CRC Press, 2012. Google Scholar
Digital Library
- F. Taroni, C.G. Aitken, P. Garbolino, and A. Biedermann. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science. Wiley, 2014.Google ScholarCross Ref
- N. Fenton, M. Neil, and D. Berger. Bayes and the law. Annual Review of Statistics and Its Application, 3: 51--77, 2016.Google ScholarCross Ref
- B. Yet, Z.B. Perkins, N.R.M. Tai, and D.W.R. Marsh. Clinical evidence framework for Bayesian networks. Knowledge and Information Systems, 50(1): 117--143, 2017. Google ScholarDigital Library
- S. Modgil and H. Prakken. A general account of argumentation with preferences. Artificial Intelligence, 195: 316--397, 2013. Google ScholarDigital Library
- J. Keppens. On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning. Artificial Intelligence and Law, 22(3): 239--290, 2014. Google ScholarDigital Library
- H. Prakken. A new use case for argumentation support tools: supporting discussions of Bayesian analyses of complex criminal cases. Artificial Intelligence and Law.Google Scholar
- F.V. Jensen and T.D. Nielsen. Bayesian Networks and Decision Graphs. Springer, 2nd ed., 2007. Google ScholarDigital Library
- D. Walton, C. Reed, and F. Macagno. Argumentation Schemes. Cambridge University Press, 2008.Google ScholarCross Ref
- P.M. Dung. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games. Artificial Intelligence, 77(2): 321--357, 1995. Google ScholarDigital Library
- G. Doekhie. A Bayesian network for assigning probabilities on which finger left a mark. Master's thesis. University of Amsterdam, The Netherlands, 2012.Google Scholar
- R. Haraksim, D. Meuwly, G. Doekhie, P. Vergeer, and M. Sjerps. Assignment of the evidential value of a fingermark general pattern using a Bayesian network. In A. Brömme and C. Busch, eds., Proceedings of the 12th International Conference of the Biometrics Special Interest Group, volume 212, pages 99--109. GI, 2012.Google Scholar
- V. Doshi. Validation of Bayesian networks with a case study on fingerprint general patterns. Master's thesis. Utrecht University, The Netherlands, 2013.Google Scholar
- J. Pitchforth and K. Mengersen. A proposed validation framework for expert elicited Bayesian networks. Expert Systems with Applications, 40(1): 162--167, 2013. Google ScholarDigital Library
- S. Modgil and H. Prakken. Resolutions in structured argumentation. In B. Verheij, S. Woltran, and S. Szeider, eds., Computational Models of Argument: Proceedings of COMMA 2012, volume 245, pages 310--321. IOS Press, 2012.Google Scholar
- F. Bex andS. Renooij. From arguments to constraints on a Bayesian network. In P. Baroni, T.F. Gordon, T. Scheffler, and M. Stede, eds., Computational Models of Argument: Proceedings of COMMA 2016, volume 287, pages 95--106. IOS press, 2016.Google Scholar
- R. Wieten, F. Bex, H. Prakken, and S. Renooij. Exploiting causality in constructing Bayesian networks from legal arguments. In M. Palmirani, ed., Legal Knowledge and Information Systems. JURIX 2018: The Thirty-first Annual Conference, volume 313, pages 151--160. IOS Press, 2018.Google Scholar
- F. Bex, S. Modgil, H. Prakken, and C.A. Reed. On logical specifications of the argument interchange format. Journal of Logic and Computation. 23(5): 951--989, 2013.Google ScholarCross Ref
- S.T. Timmer, J.-J.C. Meyer, H. Prakken, S. Renooij, and B. Verheij. A two-phase method for extracting explanatory arguments from Bayesian networks. International Journal of Approximate Reasoning, 80: 475--494, 2017. Google ScholarDigital Library
- J. Keppens. Argument diagram extraction from evidential Bayesian networks. Artificial Intelligence and Law, 20(2): 109--143, 2012. Google ScholarDigital Library
Index Terms
- Supporting Discussions About Forensic Bayesian Networks Using Argumentation
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