Probabilistic graphical models to deal with age estimation of living persons

Abstract

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

Status of Mandibular Third Molar Development as Evidence in Legal Age Threshold Cases

“…In point of fact, Sironi et al. use stopping ratios (this is clear from their Eqs and ) but refer to them as continuation ratios. Sironi et al.…”

“…Sironi et al. (:479) state this succinctly when they write: “One might believe that in such situations, the distribution of individuals for whom an examination for age estimation purpose is requested may be close to the age threshold of interest, for example, 18 years of age.” They further suggest that “there is a predominance of persons older than 18 years of age for whom an examination for age estimation is requested because, for them, it is advantageous to claim being younger than this age threshold.” While this may well be true, it is doubtful that a court would allow such prior knowledge to influence the presentation of age indicator evidence by an expert witness. With this said, the prior age distribution does play a critical role in calculating the Bayes’ factor from age indicator evidence.…”

“…Sironi et al. ignore this issue and instead use asymmetric prior distributions, specifically the skew‐normal distribution, that place greater weight on the prior probability that the defendant is over the legal threshold age. Following Azzalini , Sironi et al.…”

“…For example, in the case of R (on the application of ZM and SK) v The London Borough of Croydon (Dental age assessment) [2016] UKUT 00559 (IAC), the court found that: “The fact that all teeth are mature in the sense that all have reached Demirjian stage H is a sign of chronological maturity but is not a reliable indicator of whether an individual is more or less than 18 years old.” This quote confuses chronological maturity with biological maturity, but in any event it recognizes the problem of using dental development in evidentiary settings. Outside of the courts, using dental formation in an evidentiary setting for problems that involve assessing whether an individual is above, or in some cases below, a particular age threshold has received considerable recent interest in the literature. However, there is neither consensus on what methods to use, nor uniformity of opinion on whether or not the dentition is informative enough to be used in age threshold determinations.…”

“…Sironi et al. have greatly clarified the age threshold problem by carefully treating the prior odds, the dental evidence, and the posterior odds. Our paper extends their work by distinguishing the prior odds for a case (the a priori probability that the individual in question is at or over the legal age threshold as vs. below it) from the prior density for an age distribution.…”