Forensic reports use various types of conclusions, such as a categorical (CAT) conclusion or a likelihood ratio (LR). In order to correctly assess the evidence, users of forensic reports need to understand the conclusion and its evidential strength. The aim of this paper is to study the interpretation of the evidential strength of forensic conclusions by criminal justice professionals. In an online questionnaire 269 professionals assessed 768 reports on fingerprint examination and answered questions that measured self-proclaimed and actual understanding of the reports and conclusions. The reports entailed CAT, verbal LR and numerical LR conclusions with low or high evidential strength and were assessed by crime scene investigators, police detectives, public prosecutors, criminal lawyers, and judges. The results show that about a quarter of all questions measuring actual understanding of the reports were answered incorrectly. The CAT conclusion was best understood for the weak conclusions, the three strong conclusions were all assessed similarly. The weak CAT conclusion correctly emphasizes the uncertainty of any conclusion type used. However, most participants underestimated the strength of this weak CAT conclusion compared to the other weak conclusion types. Looking at the self-proclaimed understanding of all professionals, they in general overestimated their actual understanding of all conclusion types.
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This chapter explores the legal and moral implications of the use of data science in criminal justice at two levels: police surveillance and the criminal trial of a defendant. At the first level, police surveillance, data science is used to identify places and people at high risk of criminal activity, allowing police officers to target surveillance and take proactive measures to try to prevent crime (predictive policing). At the second level, the criminal trial of a defendant, data science is used to make risk assessments to support decisions about bail, sentencing, probation, and supervision and detention orders for high-risk offenders. The use of data science at these levels has one thing in common: it is about predicting risk. The uncertainty associated with risk prediction raises specific related legal and ethical dilemmas, for example in the areas of reasonable suspicion, presumption of innocence, privacy, and the principle of non-discrimination.
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This critical, literature-review based research project, inspired by the outbreak of the Russia-Ukraine war, examines the limitations and possibilities of restorative justice in a time of war. Any armed conflict creates and amplifies the need for extreme militarisation and securitisation, accompanied by belligerent rhetoric. Thus, for restorative justice scholars and practitioners, the outbreak of war challenges the applicability of restorative justice values and practices, as bipolar interpretations of events, conflicts, and human suffering displace more balanced views. The purpose of our research is to critically discuss the applicability of restorative justice in times of war and in the context of the Russo-Ukrainian War in particular. Our motivation to focus on this specific war and to examine the (im)possibilities of restorative justice from Eurocentric perspective stems from three observations: (1) In the last 20 years restorative justice was continuously promoted in Europe as a new “culture” of justice; (2) The Russo-Ukrainian War currently takes place on the European continent and impacts the European security architecture more than that of other world regions; (3) This war has a particular meaning to the world (e.g., a violent clash between the (former) Cold War superpowers, an element of surprise, the shattered myth of overwhelming Russian military might, the nuclear threat coupled with a global energy crisis etc.).
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