TY - JOUR
T1 - Analysis of Evidence in International Criminal Trials Using Bayesian Belief Networks
AU - McDermott, Yvonne
AU - Aitken, Colin
PY - 2017/9/4
Y1 - 2017/9/4
N2 - This article demonstrates how different actors in international criminal trials could utilise Bayesian Networks (‘Bayes Nets’), which are graphical models of the probabilistic relationships between hypotheses and pieces of evidence. We argue that Bayes Nets are potentially useful in both the examination of international criminal judgments and the processes of trial preparation and fact-finding before international criminal tribunals. With the use of a practical case study based on a completed case from the International Criminal Tribunal for the former Yugoslavia (ICTY), we illustrate how Bayes Nets could be used by international criminal tribunals to strengthen judges' confidence in their findings, to assist lawyers in preparing for trial, and to provide a tool for the assessment of international criminal tribunals' factual findings.
AB - This article demonstrates how different actors in international criminal trials could utilise Bayesian Networks (‘Bayes Nets’), which are graphical models of the probabilistic relationships between hypotheses and pieces of evidence. We argue that Bayes Nets are potentially useful in both the examination of international criminal judgments and the processes of trial preparation and fact-finding before international criminal tribunals. With the use of a practical case study based on a completed case from the International Criminal Tribunal for the former Yugoslavia (ICTY), we illustrate how Bayes Nets could be used by international criminal tribunals to strengthen judges' confidence in their findings, to assist lawyers in preparing for trial, and to provide a tool for the assessment of international criminal tribunals' factual findings.
U2 - https://academic.oup.com/lpr/article-abstract/16/2-3/111/4103450?redirectedFrom=fulltext
DO - https://academic.oup.com/lpr/article-abstract/16/2-3/111/4103450?redirectedFrom=fulltext
M3 - Article
SN - 1470-8396
VL - 16
SP - 111
EP - 129
JO - Law, Probability & Risk
JF - Law, Probability & Risk
IS - 2-3
ER -