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.
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.
Project objectives Radicalisation research leads to ethical and legal questions and issues. These issues need to be addressed in way that helps the project progress in ethically and legally acceptable manner. Description of Work The legal analysis in SAFIRE addressed questions such as which behavior associated with radicalisation is criminal behaviour. The ethical issues were addressed throughout the project in close cooperation between the ethicists and the researchers using a method called ethical parallel research. Results A legal analysis was made about criminal law and radicalisation. During the project lively discussions were held in the research team about ethical issues. An ethical justification for interventions in radicalisation processes has been written. With regard to research ethics: An indirect informed consent procedure for interviews with (former) radicals has been designed. Practical guidelines to prevent obtaining information that could lead to indirect identification of respondents were developed.
