Forensic identification: the Island Problem and its generalisations

TL;DR

This paper thoroughly analyzes the classical and generalized Island Problem in forensic identification, emphasizing the importance of conditioning and likelihood ratios in evaluating suspect-criminal probabilities under various complexities.

Contribution

It extends the classical Island Problem to heterogeneous populations, biased searches, and uncertainties, providing a comprehensive framework for forensic probability assessment.

Findings

Conditioning significantly affects likelihood ratios.

Generalizations accommodate population heterogeneity and search biases.

Posterior probabilities often remain consistent across hypotheses.

Abstract

In forensics it is a classical problem to determine, when a suspect $S$ shares a property $\Gamma$ with a criminal $C$, the probability that $S=C$. In this paper we give a detailed account of this problem in various degrees of generality. We start with the classical case where the probability of having $\Gamma$, as well as the a priori probability of being the criminal, is the same for all individuals. We then generalize the solution to deal with heterogeneous populations, biased search procedures for the suspect, $\Gamma$-correlations, uncertainty about the subpopulation of the criminal and the suspect, and uncertainty about the $\Gamma$-frequencies. We also consider the effect of the way the search for $S$ is conducted, in particular when this is done by a database search. A returning theme is that we show that conditioning is of importance when one wants to quantify the "weight" of the evidence by a likelihood ratio. Apart from these mathematical issues, we also discuss the practical problems in applying these issues to the legal process. The posterior probabilities of $C=S$ are typically the same for all reasonable choices of the hypotheses, but this is not the whole story. The legal process might force one to dismiss certain hypotheses, for instance when the relevant likelihood ratio depends on prior probabilities. We discuss this and related issues as well. As such, the paper is relevant both from a theoretical and from an applied point of view.