Summary of Murdock et. al
Introduction
There have been much criticism of how accurately evidence can be presented in the field of firearms and toolmark evidences. One main concern within this field is that the community has not yet succeeded in supplying the “known or potential error rate. From a statistical point of view, the nature of toolmark surfaces makes probability densities nearly impossible to estimate in an accurate manner. Random match probabilities cannot be estimated easily because of the spectrum of variability of data. Many efforts have been made in order to estimate RMPs and identification error rates. Quantifiable measures of uncertainty of forensic analyses have been recommended by the community, but the relevance of these mathematical functions is another matter. Critics argue that this field does not have a strong statistical basis of toolmark identification. Statistical models have in fact been implemented to firearms and toolmark identification. However, each model has its own limitations. Each model needs more further testing in order to see the accuracy of it. This is also true in various other fields of forensic science such as fingerprints, trace evidences, shoe prints or tire tracks. Forensic science aside, other important aspects lack robust models such as the appointment of judges, the prediction of weather and the future of criminal behavior of a convict.
Review of the Current Literature in Statistical Applications for Firearms and Toolmarks
Firearm and toolmark identification has been developing and growing since the 1930s. There have been numerous studies designed to develop and test the statistical probabilities of finding a match (of either a gun or bullet) by random chance. Other studies were developed to help increase the need for empirical based studies. The progression of technology has led to examiners being able to scan and analyze firearm and toolmark RMPs (random match probability) in 3D. This allows for the potential creation of automated computer programs that can scan for RMPs, thus decreasing human error.
Current State of the Use of RMPs in Firearm and Toolmark Identification
In this section of the ariticle it discusses how RMPs are usually “error rates” and that these rates are hard to establish for firearm and toolmark identification. They also talk about how they do not know at this time if whether it is although feasible to apply RMP to the identification of firearms and toolmarks. Tool markings also help play a part in why they say RMPs are not reliable, they even discuss how it will be extremely difficult to develop a universal mathematical model that can accurately predict the random toolmarks left by some tools.
Absolute Versus Practical Identification and Subjectivity
This section is about how whether certain evidence is going to be relevant for use or not. The writers here talk about the concept of uniqueness and the allowed use of “absolute doubt” but the forbidden use of “reasonable certainty.” The writers continue to state that subjective aspects in firearms and toolmark identification are seen as flaws in examination which means they are not reliable enough. Subjective is used when examiners determine similarities is good enough for identification or not. It has also made it’s way up to the courtrooms when making decisions. The writers then end the paper saying that subjectivity has a used in all actions and opinions of both the system of justice and in our everyday lives.
Title & Abstract Discussion
The Abstract and title had nothing to do with this article at all. I believe the abstract is misleading the article because throughout the article the authors weren’t talking about aything relative to RMPs, they were just bashing other researchers work and studies saying its not reliable and no help to the issue of criticism toward firearms and toolmark analysis. This article was just really all a big lie.