The DOI matches its Crossref record.
A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation
Abstract
This Guideline proposes a protocol for the validation of forensic evaluation methods at the source level, using the Likelihood Ratio framework as defined within the Bayes' inference model. In the context of the inference of identity of source, the Likelihood Ratio is used to evaluate the strength of the evidence for a trace specimen, e.g. a fingermark, and a reference specimen, e.g. a fingerprint, to originate from common or different sources. Some theoretical aspects of probabilities necessary for this Guideline were discussed prior to its elaboration, which started after a workshop of forensic researchers and practitioners involved in this topic. In the workshop, the following questions were addressed: "which aspects of a forensic evaluation scenario need to be validated?", "what is the role of the LR as part of a decision process?" and "how to deal with uncertainty in the LR calculation?". The questions: "what to validate?" focuses on the validation methods and criteria and "how to validate?" deals with the implementation of the validation protocol. Answers to these questions were deemed necessary with several objectives. First, concepts typical for validation standards [1], such as performance characteristics, performance metrics and validation criteria, will be adapted or applied by analogy to the LR framework. Second, a validation strategy will be defined. Third, validation methods will be described. Finally, a validation protocol and an example of validation report wil…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The Likelihood Ratio (LR) comes as a possible solution [26,36] for standardization, expressing the decision as the ratio of the probability given the evidence of a match against the probability of a non-match.”
From Calibration of score based likelihood ratio estimation in automated forensic facial image comparison (2022) · cites this paper as [26]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 37
- Ellison, 2000, Uncertainties in qualitative testing and analysis, Accredit. Qual. Assur., 5, 346, 10.1007/s007690000212 · DOI
- Meuwly, 2006, Forensic individualization from biometric data, Sci. Just., 46, 205, 10.1016/S1355-0306(06)71600-8 · DOI
- Lindley, 1977, A problem in forensic science, Biometrika, 64, 207, 10.1093/biomet/64.2.207 · DOI
- Bolck, 2009, Different likelihood ratio approaches to evaluate the strength of evidence of MDMA tablet comparisons, Forensic Sci. Int., 191, 42, 10.1016/j.forsciint.2009.06.006 · DOI
- Gonzalez-Rodriguez, 2007, Emulating DNA: rigorous quantification of evidential weight in transparent and testable forensic speaker recognition, IEEE Trans. Audio Speech Lang. Process., 15, 2104, 10.1109/TASL.2007.902747 · DOI
- Ali, 2012, A review of calibration methods for biometric systems in forensic applications, 126
- Alberink, 2013, Fingermark evidence evaluation based on automated fingerprint identification system matching scores: the effect of different types of conditioning on likelihood ratios, J. Forensic Sci.
- Hepler, 2012, Score-based likelihood ratios for handwriting evidence, Forensic Sci. Int., 219, 129, 10.1016/j.forsciint.2011.12.009 · DOI
- Neumann, 2015, Quantifying the weight of fingerprint evidence through the spatial relationship, directions and types of minutiae observed on fingermarks, Forensic Sci. Int., 248, 154, 10.1016/j.forsciint.2015.01.007 · DOI
- Martyna, 2013, Analysis of lead isotopic ratios of glass objects with the aim of comparing them for forensic purposes, Talanta, 105, 158, 10.1016/j.talanta.2012.11.079 · DOI
- Ramos, 2011, Information-theoretical feature selection using data obtained by Scanning Electron Microscopy coupled with and Energy Dispersive X-ray spectrometer for the classification of glass traces, Anal. Chim. Acta, 705, 207, 10.1016/j.aca.2011.05.029 · DOI
- Evett, 1998, Toward a uniform framework for reporting opinions in forensic science casework, Sci. Just., 38, 198, 10.1016/S1355-0306(98)72105-7 · DOI
- Association of the Forensic Science Providers (AFSP), 2009, Standards for the formulation of evaluative forensic science expert opinion, Sci. Just., 49, 161, 10.1016/j.scijus.2009.07.004 · DOI
- Gill, 2006, DNA commission of the International Society of Forensic Genetics: recommendations on the interpretation of mixtures, Forensic Sci. Int., 160, 90, 10.1016/j.forsciint.2006.04.009 · DOI
- Berger, 2011, Evidence evaluation: a response to the court of appeal judgment in R v T, Sci. Just., 51, 43, 10.1016/j.scijus.2011.03.005 · DOI
- Cook, 1998, A method for case assessment and interpretation, Sci. Just., 38, 151, 10.1016/S1355-0306(98)72099-4 · DOI
- Neumann, 2011, Quantitative assessment of evidential weight for fingerprint comparison i. generalization to the comparison of a mark with a set of ten prints from a suspect, Forensic Sci. Int., 207, 101, 10.1016/j.forsciint.2010.09.006 · DOI
- Champod, 2001, A probabilistic approach to fingerprint evidence, J. Forensic Identif., 51, 101
- Champod, 2001, Earmarks as evidence: a critical review, J. Forensic Sci., 46, 1275, 10.1520/JFS15146J · DOI
- Champod, 2000, The inference of identity in forensic speaker recognition, Speech Commun., 31, 193, 10.1016/S0167-6393(99)00078-3 · DOI
- Hoffman, 1991, Statistical evaluation of the evidential value of human hairs possibly coming from multiple sources, J. Forensic Sci., 36, 1053, 10.1520/JFS13120J · DOI
- Champod, 2003, Firearms and tool marks identification: the Bayesian approach, AFTE J., 35, 307
- Steele, 2014, Statistical evaluation of forensic DNA profile evidence, Annu. Rev. Stat. Appl., 1, 361, 10.1146/annurev-statistics-022513-115602 · DOI
- Gill, 2013, A new methodological framework to interpret complex DNA profiles using likelihood ratios, Forensic Sci. Int.: Genet., 7, 251, 10.1016/j.fsigen.2012.11.002 · DOI
- Balding, 2013, Evaluation of mixed-source, low-template DNA profiles in forensic science, PNASs, 110, 12241, 10.1073/pnas.1219739110 · DOI
- Taylor, 2013, The interpretation of single source and mixed DNA profiles, Forensic Sci. Int.: Genet., 7, 516, 10.1016/j.fsigen.2013.05.011 · DOI
- Perlin, 2011, Validating true allele DNA mixture interpretation, J. Forensic Sci., 56, 1430, 10.1111/j.1556-4029.2011.01859.x · DOI
- COUNCIL FRAMEWORK DECISION 2009/905/JHA of 30 November 2009 on Accreditation of forensic service providers carrying out laboratory activities, Official Journal of the European Union, 9.12.2009, online http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L: 2009:322:0014:0016:EN: PD.F.
- Bureau International des Poids et Measures, 2012, International vocabulary of metrology – basic and general concepts and associated terms, JCGM, 200
- Smalldon, 1973, The calculation of discrimination power for a series of correlated attributes, J. Fortensic Sci. Soc., 13, 291, 10.1016/S0015-7368(73)70828-8 · DOI
- Morrison, 2011, Measuring the validity and reliability of forensic likelihood-ratio systems, Sci. Justice, 51, 91, 10.1016/j.scijus.2011.03.002 · DOI
- Ramos, 2013, Reliable support: measuring calibration of likelihood ratios, Forensic Sci. Int. (10), 230, 156, 10.1016/j.forsciint.2013.04.014 · DOI · Paper page
- Brümmer, 2006, Application independent evaluation of speaker detection, Comput. Speech Lang., 20, 230, 10.1016/j.csl.2005.08.001 · DOI
- Lucena-Molina, 2015, Performance of likelihood ratios considering bounds on the probability of observing misleading evidence, Law Probability Risk, 10.1093/lpr/mgu022 · DOI
- Tippett, 1968, The evidential value of the comparison of paint flakes from sources other than vehicles, J. Forensic Sci. Soc., 8, 61, 10.1016/S0015-7368(68)70442-4 · DOI
- Ramos, 2013, Information-theoretical assessment of the performance of likelihood ratio computation methods, J. Forensic Sci., 58, 1503, 10.1111/1556-4029.12233 · DOI
- Haraksim, 2015, Measuring coherence of computer-assisted likelihood ratio methods, Forensic Sci. Int., 249, 123, 10.1016/j.forsciint.2015.01.033 · DOI
Cited by
Sorted by citations
A video forensic framework for the unsupervised analysis of MP4-like file container
Iuliani, M., Shullani, D., Fontani, M., Meucci, S., & Piva, A. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2859760 · Cited by 54
Avoiding overstating the strength of forensic evidence: Shrunk likelihood ratios/Bayes factors
Morrison, G. S., & Poh, N. (2018) · Science & Justice · doi:10.1016/j.scijus.2017.12.005 · Cited by 47
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Rodriguez, A. M., Geradts, Z., & Worring, M. (2022) · Forensic Science International · doi:10.1016/j.forsciint.2022.111239 · Cited by 19
Metadata
- Authors
- Meuwly, D., Ramos, D., & Haraksim, R.
- ORCID
- Meuwly, Didier
- Venue
- Forensic Science International
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 276
- Pages
- 142-153
- ISSN
- 0379-0738
- Cited by
- 131
- References
- 0
- Language
- en
- Funders
- European Commission Marie Curie Initial Training Network
- Netherlands Forensic Institute and the ATVS Biometric Recognition Group at the Universidad Autonoma de Madrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review