How the Probability of a False Positive Affects the Value of DNA Evidence

Thompson, William C.; Taroni, Franco; Aitken, Colin

doi:10.1520/jfs2001171

Cited by 116 publications

(70 citation statements)

References 17 publications

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“…In such cases, people sometimes mistakenly equate the RMP with the probability the defendant is innocent-an error known as "the prosecutor's fallacy" (Thompson & Schumann, 1987;Balding & Donnelly, 1994;Nance & Morris, 2002, 2005Kaye, Hans, Dann, Farley, & Albertson, 2007;Murphy & Thompson, 2010;de Keijser & Elffers, 2012;Thompson, Kaasa, & Peterson, 2013). The "prosecutor's fallacy" arises from the same transposition of conditional probabilities that underlies the "source probability error" (Thompson, 1989;Thompson, Taroni, & Aitken, 2003;Thompson, Kaasa, & Peterson, 2013). People equate the RMP with the probability the defendant is innocent because they equate the RMP with the probability the defendant is not the source of an incriminating sample while also assuming that if he is the source he must be guilty and if he is not the source he must be innocent.…”

Section: Fallacious Reasoningmentioning

confidence: 99%

Lay understanding of forensic statistics: Evaluation of random match probabilities, likelihood ratios, and verbal equivalents.

Thompson¹,

Newman²

2015

Law and Human Behavior

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Forensic scientists have come under increasing pressure to quantify the strength of their evidence, but it is not clear which of several possible formats for presenting quantitative conclusions will be easiest for lay people, such as jurors, to understand. This experiment examined the way that people recruited from Amazon's Mechanical Turk (n ϭ 541) responded to 2 types of forensic evidence-a DNA comparison and a shoeprint comparison-when an expert explained the strength of this evidence 3 different ways: using random match probabilities (RMPs), likelihood ratios (LRs), or verbal equivalents of likelihood ratios (VEs). We found that verdicts were sensitive to the strength of DNA evidence regardless of how the expert explained it, but verdicts were sensitive to the strength of shoeprint evidence only when the expert used RMPs. The weight given to DNA evidence was consistent with the predictions of a Bayesian network model that incorporated the perceived risk of a false match from 3 causes (coincidence, a laboratory error, and a frame-up), but shoeprint evidence was undervalued relative to the same Bayesian model. Fallacious interpretations of the expert's testimony (consistent with the source probability error and the defense attorney's fallacy) were common and were associated with the weight given to the evidence and verdicts. The findings indicate that perceptions of forensic science evidence are shaped by prior beliefs and expectations as well as expert testimony and consequently that the best way to characterize and explain forensic evidence may vary across forensic disciplines.

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Section: Fallacious Reasoningmentioning

confidence: 99%

Lay understanding of forensic statistics: Evaluation of random match probabilities, likelihood ratios, and verbal equivalents.

Thompson¹,

Newman²

2015

Law and Human Behavior

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“…• decision-making criteria regarding allele designation in genetic profiles (without judgment being influenced by a knowledge of the known profiles in the file) (Krane et al, 2008;Thompson, 2009b) ; • estimation of the risk of laboratory error and its inclusion in quantified assessment of results (Koehler, 1997;Thompson et al, 2003) The dialectic between scientists and commentators is therefore constructive, encouraging transparency and recognition of the fact that contamination exists and must be controlled through appropriate quality assurance procedures. Finally, it is sufficient to bear in mind that, however technologically advanced the tests may be, they are performed by human beings who are not infallible and that errors can occur despite the existence of a safety net with a very fine mesh due to all the protocols that have been put in place (Imwinkelried, 1991).…”

Section: The Advent Of Dna Profilingmentioning

confidence: 99%

Scientific Evidence in Europe -- Admissibility, Evaluation and Equality of Arms

Champod

Vuille

2011

International Commentary on Evidence

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This study was commissioned by the European Committee on Crime Problems at the Council of Europe to describe and discuss the standards used to asses the admissibility and appraisal of scientific evidence in various member countries. After documenting cases in which faulty forensic evidence seems to have played a critical role, the authors describe the legal foundations of the issues of admissibility and assessment of the probative value in the field of scientific evidence, contrasting criminal justice systems of accusatorial and inquisitorial tradition and the various risks that they pose in terms of equality of arms. Special attention is given to communication issues between lawyers and scientific experts. The authors eventually investigate possible ways of improving the system. Among these mechanisms, emphasis is put on the adoption of a common terminology for expressing the weight of evidence. It is also proposed to adopt an harmonized interpretation framework among forensic experts rooted in good practices of logical inference.

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“…These technologies are now widely applied in forensics to link suspects to crime scenes, identify disaster victims, and establish familial relationships. Under ideal circumstances, identifying codes based on human genetic markers are expected to be unique among billions of individuals (7), although practical concerns (e.g., sample contamination and relatedness among individuals) can reduce this number considerably (8).…”

mentioning

confidence: 99%

Identifying personal microbiomes using metagenomic codes

Franzosa

Huang

Meadow

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

416

293

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Community composition within the human microbiome varies across individuals, but it remains unknown if this variation is sufficient to uniquely identify individuals within large populations or stable enough to identify them over time. We investigated this by developing a hitting set-based coding algorithm and applying it to the Human Microbiome Project population. Our approach defined body site-specific metagenomic codes: sets of microbial taxa or genes prioritized to uniquely and stably identify individuals. Codes capturing strain variation in clade-specific marker genes were able to distinguish among 100s of individuals at an initial sampling time point. In comparisons with follow-up samples collected 30-300 d later, ∼30% of individuals could still be uniquely pinpointed using metagenomic codes from a typical body site; coincidental (false positive) matches were rare. Codes based on the gut microbiome were exceptionally stable and pinpointed >80% of individuals. The failure of a code to match its owner at a later time point was largely explained by the loss of specific microbial strains (at current limits of detection) and was only weakly associated with the length of the sampling interval. In addition to highlighting patterns of temporal variation in the ecology of the human microbiome, this work demonstrates the feasibility of microbiome-based identifiability-a result with important ethical implications for microbiome study design. The datasets and code used in this work are available for download from huttenhower.sph.harvard.edu/idability. forensic genetics | microbial ecology | metagenomics | human microbiome | strain variation R ecent large-scale investigations of the human microbiome have revealed great variability in the body site-specific community structure and function of microbial organisms across healthy individuals (1, 2). In addition, it has been shown that features of the human microbiome might stably associate with individuals over substantial periods of time (3-5). These observations suggest that individuals might be uniquely and stably identified within a population based on their resident microbiota. However, to date there have been no rigorous efforts to quantitatively establish the feasibility of microbiome-based identifiability. To do so requires demonstrating (i) that one can identify a "metagenomic code" that is specific to an individual in a sample population; (ii) that the code can be robustly redetected at a later time; (iii) that the code is unlikely to erroneously match a previously unseen sample; and (iv) that such codes can be constructed for a sizeable fraction of individuals (Fig.

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How the Probability of a False Positive Affects the Value of DNA Evidence

Cited by 116 publications

References 17 publications

Lay understanding of forensic statistics: Evaluation of random match probabilities, likelihood ratios, and verbal equivalents.

Lay understanding of forensic statistics: Evaluation of random match probabilities, likelihood ratios, and verbal equivalents.

Scientific Evidence in Europe -- Admissibility, Evaluation and Equality of Arms

Identifying personal microbiomes using metagenomic codes

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