Creating the New Generation of Forensic Interpretation Models

In their report, "Strengthening Forensic Science in the United States: A Path Forward" the National Academy of Sciences (NAS) laid out not only the challenges facing the forensic science community but also put forward recommendations for improvements required alleviate the problems, disparities, and lack of mandatory standards currently facing the community.

Highlighted in this report is the concern that faulty forensic science can contribute to the wrongful convictions of innocent people. One the other side of the coin, someone who is guilty of a crime can be wrongfully acquitted.

Take for instance a hypothetical case of a suspect being connected to a crime scene by trace evidence such as glass. It would be very easy to challenge the forensic analyses by simply requesting that the analysis be done by at least five(5) different forensic labs. One can almost be 100% certain that they would have no choice but to throw out the evidence because of the inconsistency of the respective results.

It is for this reason we have taken up the NAS's challenge to develop a standard forensic interpretation methods to convincingly "demonstrate a connection between evidence and a specific individual or source". It is our hope that this project will not only supply the analyst with an interpretation of the data, but also a measure of what minimal changes in the data can result in a different interpretation.

For this project we studied current methods of forensic interpretation of glass evidence. In this subfield of forensics we have so far identified at least three separate branches of research leading to three different 'best' models: the 1995 Evett model, the 1996 Walsh model, and the 2002 Koons model. To date our research has shown that the former models are 'broken'. There are two root causes for the broken mathematical models:

1. 'Brittle' interpretations where small input changes can lead to large output changes.
2. Inappropriate assumptions about the distribution of data.

To deal with these two issues, our project proposes the use of (a) clustering algorithms (meso and ant), and (b) treatment learning. The clustering algorithms will allow us to reason about crime scene data without the knowledge of standard statistical distributions, while treatment learning offers the analyst a measure of how strongly an interpretation should be believed.

Our project will also boast of a plotting tool (CLIFF) which offers the visualization of data. The software features four(4) of the current models used in the forensic community, namely the 1995 Evett model, the 1996 Walsh model, the Seheult model and the Grove model. For each model, data can be generated randomly and plotted. Other features of CLIFF include:

• the ability to perform dimensionality reduction by applying the Fastmap algorithm
• and the ability to determine if the results gained from a particular region of space is important and well supported.

In the end, it must be made clear that our purpose is not to determine innocence or guilt, or give a 100% guarantee of a match/non-match. Instead our goal is to aid in the decision making process of forensic interpretation. We want to provide the analyst with a standard dependable tool/model which reports to them an interpretation of the crime scene data as well as the treatment learner's analysis of what minimal 'treatment' could change the interpretation. What happens after this is in the hands of the analyst.

Ultimately we want to be able to rely on the results presented in a court of law, results based on correct models accepted and used by the entire forensic community. So, if a suspect demands that test be done by five(5) different forensic labs, there should be no dramatic differences in the results.