About this Research Topic
Artificial intelligence (AI) methods and machine learning (ML) are receiving growing attention and have been applied in diverse fields ranging from computational biology to biomedical applications. In the forensic field, ML techniques have been applied in evaluating the risk of violent behavior and criminal recidivism in different contexts ranging from pretrial risk assessment to sentencing. Nevertheless, ethical concerns have emerged regarding fairness, accountability, and transparency, especially when used in criminal justice. Important issues are the presence of race and gender bias, the impossibility for the defendants to probe or challenge the recommendations the tool generates, and the risk that judges and forensic practitioners may misinterpret or misapply the scores of the algorithm.
The goal of this Research Topic is to present cutting-edge studies on the application of AI techniques in the forensic field. We aim to highlight recent advances in the use of ML in the development of diagnostic and prediction models, for example, to detect malingering or risk of violence in offenders. We will focus on the application of ML techniques and psychometric, biological, laboratory, genetic, and neuroimaging aspects of forensics psychiatric assessment from criminal or civil case perspectives. We will discuss some qualitative challenges in terms of the methodology of analysis and automated processing of judicial decisions. This Research Topic will also be dealing with ethical challenges such as those related to the need to ensure non-discrimination, the “fair process” and the values of transparency and comprehensibility of decision-making processes. Together, we anticipate that this research topic will be broadly informative to forensic psychiatrists, psychologists, ethicists, and jurists.
We will welcome original studies, systematic reviews, and meta-analyses covering the following topics:
• ML and neuroimaging in the forensic field
• Ethical aspects linked to the use of AI in the forensic field
• ML techniques in detecting malingering
• AI-based systems to support personal injury compensation
• AI and its implications in guilt, punishment, and responsibility
• The application of ML in research on violence
The goal of this Research Topic is to present cutting-edge studies on the application of AI techniques in the forensic field. We aim to highlight recent advances in the use of ML in the development of diagnostic and prediction models, for example, to detect malingering or risk of violence in offenders. We will focus on the application of ML techniques and psychometric, biological, laboratory, genetic, and neuroimaging aspects of forensics psychiatric assessment from criminal or civil case perspectives. We will discuss some qualitative challenges in terms of the methodology of analysis and automated processing of judicial decisions. This Research Topic will also be dealing with ethical challenges such as those related to the need to ensure non-discrimination, the “fair process” and the values of transparency and comprehensibility of decision-making processes. Together, we anticipate that this research topic will be broadly informative to forensic psychiatrists, psychologists, ethicists, and jurists.
We will welcome original studies, systematic reviews, and meta-analyses covering the following topics:
• ML and neuroimaging in the forensic field
• Ethical aspects linked to the use of AI in the forensic field
• ML techniques in detecting malingering
• AI-based systems to support personal injury compensation
• AI and its implications in guilt, punishment, and responsibility
• The application of ML in research on violence
Keywords: Artificial Intelligence, Machine Learning, Neuroimaging, Malingering, Violence
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
