Breaking NEWz you can UzE...
compiled by Jon Stimac
Fingerprints Lifted From Batteries
MAIL, MALAYSIA - Feb 8,
2007 ...three sets of fingerprints were lifted from a
homemade bomb found...
Forensic Techniques Used to Discover the True Colors of Prehistoric
- Feb 8, 2007 ...forensic crime lab techniques to hunt for
dyes, paint, and other decoration in prehistoric textiles...
Fingerprints on Car Lead Cops to Rape Suspect
- Feb 3,
2007 ...fingerprints on a car window led
police to a man now charged with rape...
Laxity on Fingerprints Legal
ARIZONA REPUBLIC, AZ
- Feb 10, 2007
...there's a little-known legal provision that allows schools to hire
support staff without fingerprinting them first...
Recent CLPEX Posting Activity
containing new posts
Moderated by Steve Everist
10 years is enough fight for the McKie
clpexco Sun Feb 11, 2007 1:51 pm
Toronto FIS/CFS Training Conference - February 26 - March 2
Cindy Rennie Sat Feb 10, 2007 4:32 pm
Point Of View: Point Counters and Pseudoscience
Charles Parker Sat Feb 10, 2007 2:24 pm
Where are our priorities?
Heidi Fraser Fri Feb 09, 2007 6:07 pm
IAI Certification Test
Guest Fri Feb 09, 2007 3:45 pm
Alternative digital method
Wayne Reutzel Fri Feb 09, 2007 2:29 pm
Looking For Another Book
Charles Parker Thu Feb 08, 2007 8:08 pm
ACE-V In The SOP
Charles Parker Wed Feb 07, 2007 11:25 pm
Looking for Book
S. Siegel Wed Feb 07, 2007 11:15 pm
Daubert Debate in MD
L.J.Steele Tue Feb 06, 2007 8:38 pm
Vacuum Metal Deposition Chambers
Andrew Schriever Tue Feb 06, 2007 4:45 pm
UPDATES ON CLPEX.com
Updated the Smiley Files with Smiley Bonanza 2007! We had 7 new
smiley submissions this week! Thanks to Roxanne McLean, Riverside
Sheriff's Department for one, and Cynthia Rennie from Toronto Police
Department for the other six submissions!! As always, thanks to the
Smiley Czar, Bill Wolz for his efforts, and if you want to send in your
smileys, Bill can be reached at:
Cynthia Rennie brought us her
lecture notes on the appellate process from an ABFDE seminar presentation by
Steve Horn explores the use
of statistics to support or refute erroneous identifications in combination
with crime scenes. He uses hypothetical scenarios and the Marion Ross
murder case as examples, but I encourage you to read this article in its
entirety, apply these principles to your work environment and draw out any
truths that could apply to a latent print examiner you know.
Misidentifications - Town Fingerprint Project
by Steve Horn
This imaginary exercise is to illustrate a
point I have been making for some time about the Shirley McKie perjury
allegation. A fingerprint investigation is normally started because a crime
has taken place. In the McKie case the disputed match between her and
latent print Y7 came first, then a new previously unimagined offence was
suggested to explain the match. Statistical theory seems to indicate that
the pre-existing crime is an essential part of the safety of fingerprinting.
Using a few simple calculations I can show that this is true and demonstrate
that there is no certainty at all that McKie deposited Y7.
Imagine that a
town decided to have all its citizens fingerprinted and the prints held in a
database. The town also decided to send fingerprint teams to every house to
uncover latent prints. All the latents would be checked against the
database and everyone identified would have to give an explanation for why
they had been at the location.
The results would show that the overwhelming majority of identifications
would not be disputed because the person identified would be happy to accept
that they had deposited the latent print. Only in a tiny proportion of all
the identifications would the person say that they have never been at the
location. There are two explanations for a disputed identification. Either
the person was up to no good and is lying about it, or it was a
misidentification and an innocent person is being accused of depositing the
Let's say the town has about 25,000 people and
10,000 households. In every house 400 latent prints were found. So
throughout the town 4 million identifications were made (10,000 x 400).
Let's assume that misidentifications run at the rate of 1 per million
identifications so there will be 4 misidentifications during the project
spread over the 10,000 houses. Let's also assume that crimes occurred
recently in 40 houses and in 20 of these, the criminal left a latent print.
We are now going to look
at the chances of finding a misidentification or someone lying at 3
different houses, using different methods to choose the house.
Case 1. A house is
chosen at random.
If we pick a house
by sticking a pin in a map then we are extremely unlikely to hit on one that
contains a latent print where the identification is disputed. After all,
most houses dont get burgled or contain a murder, and misidentifications
are very rare. The chances of finding a latent left during a crime are 0.2%
(20 in 10000). The chances of stumbling across a misidentified latent are
0.04% (4 in 10000).
Case 2. A house is
chosen because it contains a disputed identification.
If we choose a house
because it contains a latent print where the identification was disputed
then how can we weigh up the relative strengths of the two explanations
(lying or misidentification)? If the person identified is to be prosecuted
that is what the jury will have to do.
A good starting point is the ratio of lies to misidentifications for the
town as a whole. 20 latent prints were left by criminals and there are 4
misidentifications, a ratio of 5:1. If this was put to a jury I don't think
they would consider an 83% chance of guilt and 17% chance of innocence to be
'beyond reasonable doubt'.
But using the town's overall ratio is only the starting point. We need to
ask if anybody saw the identified person in the house and we need to look
for evidence that a crime or offence has actually taken place. If there is
no evidence of a crime and nobody saw the identified person in the house
then it is reasonable to say that the balance of probabilities shifts to be
in favour of the 'misidentification' explanation.
Case 3. A house is
chosen because it contains a crime
We choose one of the 40 houses
where a recent crime happened. We now know for sure that someone has been
in the house who will lie and if we are lucky he or she will have left a
latent print. We have said that during 20 of the 40 crimes the criminal
left a print so we have a 50% chance of finding it. The chance of finding a
misidentification is the same as a house chosen at random, 0.04%. So if an
identification is disputed in this house it is 1250 (50 / 0.04) times more
likely to be a criminal lying than a misidentification.
an error rate of only 1 in a million identifications, it is not safe to
propose that a previously unimagined offence has occurred to explain a
disputed fingerprint identification. It would only be safe to do this if
the error rate could be proved to be zero. If a crime scene is chosen
because a crime has occurred then fingerprinting is reasonably safe with an
error rate of 1 per million identifications.
The Marion Ross murder inquiry is an example of case 3 above but the Shirley
McKie perjury allegation is an example of case 2. With no other prior
evidence than the fingerprint there is no certainty that Shirley McKie is
lying and she should not have been prosecuted for perjury. This conclusion
can be reached without looking at the quality of the fingerprint work.
Instead of leading to an allegation, a disputed identification in these
circumstances should be seen as a time to question whether best practice has
I believe that this exercise shows that the SCRO fingerprint experts at
Shirley McKie's perjury trial unknowingly misled the jury because they did
not point out that McKie's identification carries no certainty. If they
misled the jury it was because they were not taught the statistical theory
that underpins their profession.
The assumptions and
purpose of this exercise is to show that the method of choosing a crime
scene has a large effect on the certainty of an identification. It might be
interesting to compare it with real fingerprint work.
10,000 crime scenes represents the whole of fingerprinting activity in an
area. I would imagine that in Scotland or the UK we will get up to that
number in a few years. In this exercise the whole town was treated as
suspects. In real fingerprint work eliminations account for the vast
majority of all the identifications. In an investigation with only one site
a misidentification during elimination will result in a latent being
erroneously eliminated but it is not likely to result in a false accusation.
In investigations with multiple sites a person on the elimination list would
be thought to be innocent at one location but could become a suspect if he
or she was identified (or misidentified) at another location.
400 latent prints in each house was chosen because that is the number of
latents in the Marion Ross murder house
Of course in an area such as Scotland over a period of years there will be a
lot more than 40 crimes. However, for the purposes of calculating the
safety of making accusations of lying when a disputed latent is found during
an investigation, and the person identified is not accused of the original
crime, it is not the original crimes that we need to estimate, it is the
number of other hidden events that would result in the identified person
lying that occur in the 10,000 crime scenes.
In this exercise each of the 4 million latents was compared against all
25,000 people in the database so 100 billion (100 thousand million)
comparisons were carried out. To achieve only 4 misidentifications our
imaginary fingerprint experts have to achieve a random match error of 1 in
25 billion. This is 4 times the population of the world.
Can we calculate the probability
that Shirley McKie is lying?
If someone is accused of
lying in similar circumstances to Shirley McKie, I think that the
probability that they are lying might be estimated as follows:
For any given area and time period divide:
The number of people who have their fingerprints
checked during investigations who visit the crime scene and will lie about
it and will leave a useable latent but leave no other evidence of their
presence and are unseen by anybody but will not be charged with the original
crime. This will be a small number and will be impossible to estimate.
The number of misidentifications from all
fingerprint activity in the area and time period except for
misidentifications during elimination where the error would not result in an
accusation. This will also be a small number and will be impossible to
This is at best a
"balance of probabilities" situation. Nobody should be accused of lying on
the ratio of two small numbers that are impossible to estimate. In a normal
crime-led accusation (case 3) we are on much more solid ground because we
would be dividing a big number (because we KNOW that the criminal visited
the crime scene the probability of finding his or her latent is high), by a
very small number (because we have minimised the chance of misidentification
by limiting the investigation to only a few hundred latents).
An on-line version of this paper is available with some more supplementary
Computer programmer working in the field of statistics for industry
10 February 2007
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