Bryan James Found (1962-2016)

Dr Bryan Found
Dr. Bryan Found, 1962-2016

 


On October 23, 2016 we lost a very good friend, long-time colleague, and mentor — Dr. Bryan Found. His passing came as a great shock and it has taken some time to process this new reality.

Bryan was truly a great guy and I considered him to be a very good friend.  I was fortunate to attend many of his lectures and workshops over the years.  Bryan had a unique approach to forensic science.  In my opinion his insights and knowledge were unequalled.  In recent years I also had the honour and privilege of working with him on various projects.
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Measurement Science and Standards in Forensic Handwriting Analysis Conference

The expression “better late than never” applies to this post. Over the span of two days in June 2013 the Measurement Science and Standards in Forensic Handwriting Analysis (MSSFHA) conference was held. It explored the (then) current state of forensic handwriting analysis, aka, forensic handwriting examination (FHE). Presentations varied in content but most discussed recent advancements in measurement science and quantitative analyses as it relates to FHE.

NIST Forensic logo

The conference was organized by NIST’s Law Enforcement Standards Office (OLES) in collaboration with the AAFS — Questioned Document Section, the ABFDE, the ASQDE, the FBI Laboratory, the NIJ and SWGDOC.

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2016 ASQDE Panel “Approaches to Evaluation and Reporting of Expert Evidence”

Every ASQDE meeting is worth attending. They are great fun with lots of useful and interesting content. Unfortunately, I could not make it to the 2016 ASQDE conference held in Pensacola, Florida.  Nonetheless I managed to participate, albeit via Skype.

2016 ASQDE panel discussion - ASQDE logoOne of the activities at the conference was a panel discussion discussing “Approaches to Evaluation and Reporting of Expert Evidence” and I was invited to participant with three other people. It was a very interesting session…
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Prior odds — their meaning and significance

The concepts of ‘prior odds’, a.k.a., prior probabilities or simply priors, and ‘posterior odds’ come up in most discussions about the evaluation of evidence. The significance and meaning of both terms becomes clear when viewed in the context of a “Bayesian approach”, or the logical approach, to evidence evaluation. That approach has been discussed at length elsewhere and relates to the updating of one’s belief about events based upon new information. A key aspect is that some existing belief, encapsulated as the ‘prior odds’ of two competing possibilities or events, will be updated on the basis of new information, encapsulated in the ‘likelihood-ratio’1 (another term you will undoubtedly have seen), to produce some new belief, encapsulated as ‘posterior odds’ about those same competing possibilities.

But what precisely do these terms, ‘prior odds’ and ‘posterior odds’, mean and how do they relate to the work of a forensic examiner?
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Hilton and Mathematical Probability

In 1958 Ordway Hilton participated in Session #5 of the RCMP Seminar Series. His article was originally published in that series by the RCMP, and subsequently republished in 1995 in the International Journal of Forensic Document Examiners.2

The later republication included the following abstract:

In every handwriting identification we are dealing with the theory of probability. If an opinion is reached that two writings are by the same person, we are saying in effect that with the identification factors considered the likelihood of two different writers having this combination of writing characteristics in common is so remote that for all practical purposes it can be disregarded. Such an opinion is derived from our experience and is made without formal reference to any mathematical measure. However, the mathematician provides us with a means by which the likelihood of chance duplication can be measured. It is the purpose of this paper to explore the possibility of applying such mathematical measure to the handwriting identification problem to see how we might quantitatively measure the likelihood of chance duplication.

Hilton’s article was written in 8 main sections with references, and is followed by a discussion between seminar participants. Today’s review will discuss each section of the article in turn.
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Huber, Headrick & Bayes…

Like many document examiners I consider Huber and Headrick’s 1999 textbook, Handwriting Identification: Facts and Fundamentals, to be a seminal work.3

Huber and Headrick Handwriting Identification

In my opinion, it is the best textbook written to date on the topic of handwriting identification. The authors provide a comprehensive overview as well as some less conventional perspectives on select concepts and topics. In general, I tend to agree with their position on many things. A bit of disclosure is need here: I was trained in the RCMP laboratory system; the same system in which Huber and Headrick were senior examiners and very influential. Hence, I tend to be biased towards their point-of-view.

That does not, however, mean that I think their textbook is perfect. While it is well written and manages to present a plethora of topics in reasonable depth, some parts are incomplete or misleading; particularly when we take developments that have happened since it was written into account.

One area of particular interest to me relates to the evaluation of evidence; specifically evaluation done using a coherent logical (or likelihood-ratio) approach.4  I have posted elsewhere on the topic so I’m not going to re-hash the background or details any more than necessary.

Rev Bayes

This post will look at the topic of ‘Bayesian concepts’ as discussed by Huber and Headrick in their textbook. These concepts fall under the general topic of statistical inference found in their Chapter 4, “The Premises for the Identification of Handwriting”.  The sub-section of interest is #21 where the authors attempt to answer the question, “What Part Does Statistical Inference Play in the Identification Process?”  Much of their answer in that sub-section relates to Bayesian philosophy, in general, and the application of the logical approach to evidence evaluation. However, while they introduce some things reasonably well, the discussion is ultimately very flawed and very much in need of correction. Or, at least, clarification.

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Propositions — key to the evaluation process

One of the key elements in the logical approach to evidence evaluation are the propositions used for the evaluation. They are, in a certain sense, the most important part of the whole process. At the same time, they are also one of the least understood.

Today’s post explores the concept of propositions. I will attempt to describe what they are, how they are used, why we don’t change them once set and why they matter so much, among other things… all from the perspective of forensic document examination (though also applicable to other forensic disciplines).

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R you ready for statistics?

The R program for statistics is an amazingly powerful and completely free program (under the terms of the Free Software Foundation’s GNU General Public License).  If you have any need to do statistics, then you really must take a look at R or, more formally, “The R Project for Statistical Computing“.

What exactly is R?  Simply put, “R is a language and environment for statistical computing and graphics.”  It is a special open-source implementation of S which is one of the earlier statistical programming languages.
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Forewarned…

Forewarned is forearmed or, if Latin is your thing, “praemonitus, praemunitus”. So the saying goes and clearly there is great value in knowing what lies ahead for us. If we know what is coming our way we can, in theory, prepare properly for any challenge.

Challenges are nothing new to forensic scientists. Critics routinely point out issues they have with our work. Some of those criticisms are fair and reasonable, others not so much. Much of the critical commentary affects a discipline as a whole demanding an overall, or group, response by members of each discipline. In my experience, disciplines tend to be behind the curve in their responses to critics. Nonetheless, over time some issues have been addressed, at least partially if not completely, through empirical research. Others have not. To be fair, the activities needed to properly address the critics are not trivial and require both time and resources; scarce commodities in modern forensic labs. Overall, things are improving, albeit very slowly.

Criticism takes on a whole new meaning in the context of a court of law. Indeed, I think that criticism is the essence of cross-examination — a fundamental and important aspect of any adversarial justice system. Although essential, it is rarely an enjoyable part of the proceedings for any expert.

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Certification — ABFDE

What is certification? In my opinion, professional certification is a designation that indicates the holder of the certification has appropriate and adequate qualifications to do some particular, generally well-defined, job or task. As an example I am a forensic document examiner and I have received professional certification from the American Board of Forensic Document Examiners, Inc.

An internet search for ‘certification’ produces a huge list of possibilities, with more such programs being developed all the time as people become attuned to issues of quality and competency. Indeed, almost every profession has some type of certification and a few have several (consider all of the ‘certifications’ in the computing industry). Most, if not all, certification programs are aimed at improving the quality in a given profession by setting minimum standards for the job. The basic idea is that someone meeting or exceeding those standards will produce quality output on the job. Certification programs are generally created or are administered by a professional society, a college or university, or some private body set up expressly for that purpose.

Forensic Document Examination is no exception so it may be worthwhile discussing certification options as well as the pros and cons that I see for those options.

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