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Interpreting Small Quantities of DNA: The Hierarchy of Propositions and the Use of Bayesian Networks

NCJ Number
Journal of Forensic Sciences Volume: 47 Issue: 3 Dated: May 2002 Pages: 520-530
Ian W. Evett D.Sc.; Peter D. Gill Ph.D.; Graham Jackson B.Sc.; Jonathan Whitaker Ph.D.; Christophe Champod Ph.D.
Date Published
11 pages
This article describes two discoveries that will assist researchers in analyzing DNA evidence: the use of the hierarchy of propositions and the use of Bayesian Networks.
The authors report that in the field of forensic science, the questions surrounding DNA analysis are shifting from “whose DNA is this” to “how did this DNA get here?” These are questions concerning recovery, transference, persistence, and contamination. This paper reports on two recent developments in the field that help to answer the new question of how DNA moves about. The first discovery is called the hierarchy of propositions and it states that the interpretation of evidence involves considering two competing propositions. According to the hierarchy of propositions, there are various levels of propositions to be considered. For example, there are offence level propositions, which generally revolve around such considerations as: the defendant committed the crime or someone else committed the crime. Also under consideration are activity level propositions: the defendant smashed the window or the defendant has never been at the crime scene. Additionally, if there is not enough evidence to answer propositions at the activity level, source level propositions must be considered; these questions revolve around where evidence came from: the broken glass came from the crime scene or the broken glass came from somewhere else. The authors offer case studies to illustrate the use of propositions when analyzing DNA evidence. The authors next turn to the second discovery under consideration in this paper: the use of Bayesian Networks. Basically, a Bayesian Network is a graph representing probabilistic relationships among a set of variables. This graph suggests the likelihood of the various considerations involved in analyzing a case with DNA evidence. The authors again offer case study examples to illustrate the use of Bayesian Networks in a case involving DNA evidence. In conclusion, the authors suggest that these two discoveries offer powerful assistance to those analyzing DNA evidence for criminal justice purposes. 9 Tables, 8 figures, 8 references