Interpreting DNA Evidence: A Review

Advancing a paradigm shift in evaluation of forensic evidence: The rise of forensic data science

Widespread practice across the majority of branches of forensic science uses analytical methods based on human perception, and interpretive methods based on subjective judgement. These methods are non-transparent and are susceptible to cognitive bias, interpretation is often logically flawed, and forensic-evaluation systems are often not empirically validated. I describe a paradigm shift in which existing methods are replaced by methods based on relevant data, quantitative measurements, and statistical models; methods that are transparent and reproducible, are intrinsically resistant to cognitive bias, use the logically correct framework for interpretation of evidence (the likelihood-ratio framework), and are empirically validated under casework conditions.

Using the likelihood ratio in bloodstain pattern analysis

There is an apparent paradox that the likelihood ratio (LR) approach is an appropriate measure of the weight of evidence when forensic findings have to be evaluated in court, while it is typically not used by bloodstain pattern analysis (BPA) experts. This commentary evaluates how the scope and methods of BPA relate to several types of evaluative propositions and methods to which LRs are applicable. As a result of this evaluation, we show how specificities in scope (BPA being about activities rather than source identification), gaps in the underlying science base, and the reliance on a wide range of methods render the use of LRs in BPA more complex than in some other forensic disciplines. Three directions are identified for BPA research and training, which would facilitate and widen the use of LRs: research in the underlying physics; the development of a culture of data sharing; and the development of training material on the required statistical background. An example of how recent fluid dynamics research in BPA can lead to the use of LR is provided. We conclude that an LR framework is fully applicable to BPA, provided methodic efforts and significant developments occur along the three outlined directions.

Strength of linguistic text evidence: A fused forensic text comparison system

Compared to other forensic comparative sciences, studies of the efficacy of the likelihood ratio (LR) framework in forensic authorship analysis are lagging. An experiment is described concerning the estimation of strength of linguistic text evidence within that framework. The LRs were estimated by trialling three different procedures: one is based on the multivariate kernel density (MVKD) formula, with each group of messages being modelled as a vector of authorship attribution features; the other two involve N-grams based on word tokens and characters, respectively. The LRs that were separately estimated from the three different procedures are logistic-regression-fused to obtain a single LR for each author comparison. This study used predatory chatlog messages sampled from 115 authors. To see how the number of word tokens affects the performance of a forensic text comparison (FTC) system, token numbers used for modelling each group of messages were progressively increased: 500, 1000, 1500 and 2500 tokens. The performance of the FTC system is assessed using the log-likelihood-ratio cost (C_llr), which is a gradient metric for the quality of LRs, and the strength of the derived LRs is charted as Tippett plots. It is demonstrated in this study that (i) out of the three procedures, the MVKD procedure with authorship attribution features performed best in terms of C_llr, and that (ii) the fused system outperformed all three of the single procedures. When the token length is 1500, for example, the fused system achieved a C_llr value of 0.15. Some unrealistically strong LRs were observed in the results. Reasons for these are discussed, and a possible solution to the problem, namely the empirical lower and upper bound LR (ELUB) method is trialled and applied to the LRs of the best-achieving fusion system.

Bayes and the Law

Although the last forty years has seen considerable growth in the use of statistics in legal proceedings, it is primarily classical statistical methods rather than Bayesian methods that have been used. Yet the Bayesian approach avoids many of the problems of classical statistics and is also well suited to a broader range of problems. This paper reviews the potential and actual use of Bayes in the law and explains the main reasons for its lack of impact on legal practice. These include misconceptions by the legal community about Bayes' theorem, over-reliance on the use of the likelihood ratio and the lack of adoption of modern computational methods. We argue that Bayesian Networks (BNs), which automatically produce the necessary Bayesian calculations, provide an opportunity to address most concerns about using Bayes in the law.

Resolving Sample Traces in Complex Mixtures with Microarray Analyses

This chapter reviews the microarray technology and deal with the majority of aspects regarding microarrays. It focuses on today’s knowledge of separation techniques and methodologies of complex signal, i.e. samples. Overall, the chapter reviews the current knowledge on the topic of microarrays and presents the analyses and techniques used, which facilitate such approaches. It starts with the theoretical framework on microarray technology; second, the chapter gives a brief review on statistical methods used for microarray analyses, and finally, it contains a detailed review of the methods used for discriminating traces of nucleic acids within a complex mixture of samples.

Identification and separation of DNA mixtures using peak area information

We introduce a new methodology, based upon probabilistic expert systems, for analysing forensic identification problems involving DNA mixture traces using quantitative peak area information. Peak area is modelled with conditional Gaussian distributions. The expert system can be used for ascertaining whether individuals, whose profiles have been measured, have contributed to the mixture. It can also be used to predict DNA profiles of unknown contributors by separating the mixture into its individual components. The potential of our probabilistic methodology is illustrated on case data examples and compared with alternative approaches. The advantages are that identification and separation issues can be handled in a unified way within a single probabilistic model and the uncertainty associated with the analysis is quantified. Further work, required to bring the methodology to a point where it could be applied to the routine analysis of casework, is discussed.