“Because the computer said so!”: Can computational authorship analysis be trusted?

This study belongs to the domain of authorship analysis (AA), a discipline under the umbrella of forensic linguistics in which writing style is analysed as a means of authorship identification. Due to advances in natural language processing and machine learning in recent years, interest in computational methods of AA is gaining over traditional stylistic analysis by human experts. It may only be a matter of time before the software will assist, if not replace, a forensic examiner. But can we trust its verdict? The existing computational methods of AA receive critique for the lack of theoretical motivation, black box methodologies and controversial results, and ultimately, many argue that these are unable to deliver viable forensic evidence. The study replicates a popular algorithm of computational AA in order to open one of the existing black boxes. It takes a closer look at the so-called “bag-of-words” (BoW) approach – a word distributions method used in the majority of AA models, evaluates the parameters that the algorithm bases its conclusions on and offers detailed linguistic explanations for the statistical results these discriminators produce. The framework behind the design of this study draws on multidimensional analysis – a multivariate analytical approach to linguistic variation. By building on the theory of systemic functional linguistics and variationist sociolinguistics, the study takes steps toward solving the existing problem of the theoretical validity of computational AA

Anger and assaultiveness of male forensic patients with developmental disabilities : links to volatile parents

This study with 107 male forensic patients with developmental disabilities investigated whether exposure to parental anger and aggression was related to anger and assaultiveness in a hospital, controlling for background variables. Patient anger and aggression were assessed by self-report, staff-ratings, and archival records. Exposure to parental anger/aggression, assessed by a clinical interview, was significantly related to patient self-reported anger, staff-rated anger and aggression, and physical assaults in hospital, controlling for age, intelligence quotient, length of hospital stay, violent offense history, and childhood physical abuse. Results are consonant with previous findings concerning detrimental effects of witnessing parental violence and with the theory on acquisition of cognitive scripts for aggression. Implications for clinical assessment and cognitive restructuring in anger treatment are discussed

Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective

Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ‘A.I. neuroprediction,’ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed

Evaluation of forensic DNA traces when propositions of interest relate to activities: analysis and discussion of recurrent concerns

When forensic scientists evaluate and report on the probative strength of single DNA traces, they commonly rely on only one number, expressing the rarity of the DNA profile in the population of interest. This is so because the focus is on propositions regarding the source of the recovered trace material, such as “the person of interest is the source of the crime stain.” In particular, when the alternative proposition is “an unknown person is the source of the crime stain,” one is directed to think about the rarity of the profile. However, in the era of DNA profiling technology capable of producing results from small quantities of trace material (i.e., non-visible staining) that is subject to easy and ubiquitous modes of transfer, the issue of source is becoming less central, to the point that it is often not contested. There is now a shift from the question “whose DNA is this?” to the question “how did it get there?” As a consequence, recipients of expert information are now very much in need of assistance with the evaluation of the meaning and probative strength of DNA profiling results when the competing propositions of interest refer to different activities. This need is widely demonstrated in day-to-day forensic practice and is also voiced in specialized literature. Yet many forensic scientists remain reluctant to assess their results given propositions that relate to different activities. Some scientists consider evaluations beyond the issue of source as being overly speculative, because of the lack of relevant data and knowledge regarding phenomena and mechanisms of transfer, persistence and background of DNA. Similarly, encouragements to deal with these activity issues, expressed in a recently released European guideline on evaluative reporting (Willis et al., 2015), which highlights the need for rethinking current practice, are sometimes viewed skeptically or are not considered feasible. In this discussion paper, we select and discuss recurrent skeptical views brought to our attention, as well as some of the alternative solutions that have been suggested. We will argue that the way forward is to address now, rather than later, the challenges associated with the evaluation of DNA results (from small quantities of trace material) in light of different activities to prevent them being misrepresented in court

A Forensically Sound Adversary Model for Mobile Devices

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device

Replication of Known Dental Characteristics in Porcine Skin: Emerging Technologies for the Imaging Specialist

This study demonstrates that it is sometimes possible to replicate patterns of human teeth in pig skin and determine scientifically that a given injury pattern (bite mark) correlates with the dentitions of a very small proportion of a population dataset, e.g., 5 percent or even 1 percent. The authors recommend building on the template of this research with a sufficiently large database of samples that reflects the diverse world population. They also envision the development of a sophisticated imaging software application that enables forensic examiners to insert parameters for measurement, as well as additional methods of applying force to produce bite marks for research. The authors further advise that this project is applied science for injury pattern analysis and is only foundational research that should not be cited in testimony and judicial procedures. It supplements but does not contradict current guidelines of the American Board of Forensic Odontology regarding bite mark analysis and comparisons. A much larger population database must be developed. The project’s methodology is described in detail, accompanied by 11 tables and 41 figures

Lab Retriever: a software tool for calculating likelihood ratios incorporating a probability of drop-out for forensic DNA profiles.

BackgroundTechnological advances have enabled the analysis of very small amounts of DNA in forensic cases. However, the DNA profiles from such evidence are frequently incomplete and can contain contributions from multiple individuals. The complexity of such samples confounds the assessment of the statistical weight of such evidence. One approach to account for this uncertainty is to use a likelihood ratio framework to compare the probability of the evidence profile under different scenarios. While researchers favor the likelihood ratio framework, few open-source software solutions with a graphical user interface implementing these calculations are available for practicing forensic scientists.ResultsTo address this need, we developed Lab Retriever, an open-source, freely available program that forensic scientists can use to calculate likelihood ratios for complex DNA profiles. Lab Retriever adds a graphical user interface, written primarily in JavaScript, on top of a C++ implementation of the previously published R code of Balding. We redesigned parts of the original Balding algorithm to improve computational speed. In addition to incorporating a probability of allelic drop-out and other critical parameters, Lab Retriever computes likelihood ratios for hypotheses that can include up to four unknown contributors to a mixed sample. These computations are completed nearly instantaneously on a modern PC or Mac computer.ConclusionsLab Retriever provides a practical software solution to forensic scientists who wish to assess the statistical weight of evidence for complex DNA profiles. Executable versions of the program are freely available for Mac OSX and Windows operating systems

Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors

Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Evidence-Informed Criminal Justice

The American criminal justice system is at a turning point. For decades, as the rate of incarceration exploded, observers of the American criminal justice system criticized the enormous discretion wielded by key actors, particularly police and prosecutors, and the lack of empirical evidence that has informed that discretion. Since the 1967 President’s Commission on Law Enforcement and Administration of Justice report, The Challenge of Crime in a Free Society, there has been broad awareness that the criminal system lacks empirically informed approaches. That report unsuccessfully called for a national research strategy, with an independent national criminal justice research institute, along the lines of the National Institutes of Health. Following the report, police agencies continued to base their practices on conventional wisdom or “tried-and-true” methods. Prosecutors retained broad discretion, relying on their judgment as lawyers and elected officials. Lawmakers enacted new criminal statutes, largely reacting to the politics of crime and not empirical evidence concerning what measures make for effective crime control. Judges interpreted traditional constitutional criminal procedure rules in deference to the exercise of discretion by each of these actors. Very little data existed to test what worked for police or prosecutors, or to protect individual defendants’ rights. Today, criminal justice actors are embracing more data-driven approaches. This raises new opportunities and challenges. A deep concern is whether the same institutional arrangements that produced mass incarceration will use data collection to maintain the status quo. Important concerns remain with relying on data, selectively produced and used by officials and analyzed in nontransparent ways, without sufficient review by the larger research and policy community. Efforts to evaluate research in a systematic and interdisciplinary fashion in the field of medicine offer useful lessons for criminal justice. This Article explores the opportunities and concerns raised by a law, policy, and research agenda for an evidence-informed criminal justice system