The System of Forensic Activity Digitalization Theory

The article examines the system of private theory of forensic activity digitalization from the standpoint of forensic expertology. The subject, objects, tasks of the theory and its place in forensic expertology are described. The paper presents the theory of forensic activity digitalization that can be attributed to several private theories, the provisions of which equally apply both to the process of expert examination in general and to expert studies of individual types of examinations as well. Two sections in the theory system are designated: forensic examination of digital footprints and information and computer support for forensic activity. The first section includes and considers the following: the nature of digital footprints, their properties and features and the mechanism of formation; forms of presentation and classification of digital footprints and their carriers as objects of forensic examinations; general tasks of digital footprints expert examination. The second section pays attention to the technologies of algorithmizing and digitalization of methods and techniques of forensic examination. The prospects for the introduction of neural networks to forensic examination and the related relevant problems are considered. In addition, the author notes changes in the methodology and technologies for developing expert methods in connection with the introduction of artificial intelligence algorithms. This section outlines the areas of application of neural networks for solving forensic problems. The reasons for erroneous conclusions related to application of neural networks in forensic examinations are analyzed. Particular attention is paid to the sources of model risk. To develop methods for solving typical expert problems based on neural networks it is proposed to create forensic datasets and repositories for consequent analysis and machine learning for various types of forensic examinations. The article also substantiates the need for new expert competencies: expert data analyst, expert data engineer and machine learning engineer.