The modern capabilities of computers have returned interest in artificial intelligence technologies. A particular area of application of these technologies is pattern recognition, which can be applied to the traditional forensic task – identification of signs of forgery (imitation) of a signature. The results of forgery are differentiated into three types: auto-forgery, simple and skilled forgeries. Only skilled forgeries are considered in this study. The online and offline approaches to the study of signatures and other handwriting material are described. The developed artificial intelligence system based on an artificial neural network refers to the offline type of signature recognition – that is, it is focused on working exclusively with the consequences of the signature – its graphic image. The content and principles of the formation of a hypothesis for the development of an artificial intelligence system are described with a combination of humanitarian (legal) knowledge and natural-technical knowledge. At the initial stage of the study, in order to develop an experimental-applied artificial intelligence system based on an artificial neural network focused on identifying forged signatures, 127 people were questioned in order to identify a person's ability to detect fake signatures. It was found that under experimental conditions the probability of a correct determination of the originality or forgery of the presented signature for the respondent is on average 69.29 %. Accordingly, this value can be used as a threshold for determining the effectiveness of the developed artificial intelligence system. In the process of preparing the dataset (an array for training and verification of its results) of the system in terms of fraudulent signatures, some forensically significant features were revealed, associated with the psychological and anatomical features of the person performing the forgery, both known to criminalistics and new ones. It is emphasized that the joint development of artificial intelligence systems by the methods of computer science and criminalistics can generate additional results that may be useful outside the scope of the research tasks.

The paper examines the technological basis and opportunities for the use of artificial intelligence systems in law enforcement. The authors describe the investigation methods and the essence of artificial intelligence, and conduct a detailed study of approaches to the taxonomy of its systems. Artificial intelligence today does not only make it possible to solve specific tasks, but also approaches human cognition in its functions. In the present legal environment, programming and automation of crime investigation and solving are used to create information and reference systems, as well as databases and criminalistic algorithms that optimize, for example, the process of developing and verifying criminalistic leads, planning an investigation, supporting the maintenance of order, searching for the culprit, etc. The authors define key features of artificial neural networks viewed as one of the main methods of using artificial intelligence systems in law enforcement, specifically, situational adaptive learning, ability to identify non-obvious links and regularities. The designing of an applied artificial neural network is examined stage-by-stage. At the first stage, a dataset is collected - it is a volume of data for training the network. At the second stage, an algorithm (a set of rules) for learning is selected or designed. After that comes the process of learning and validating its results. The authors analyze the criteria for evaluating the effectiveness of training an artificial intelligence system, including the criteria of precision and accuracy. They single out three key types of operations in the sphere of law enforcement that can be performed by artificial intelligence systems: identification (of visual images and links between the objects of criminalistic study), prediction and classification.

Investigations of complex crimes with digital evidence increasingly require the use of modern digital devices and computer programs. Working with big data involves the accumulation, processing, and analysis of forensic information for further algorithmization and modeling of investigative actions, as well as the automation of the organizational activities of investigators. The article substantiates the need for the use of digital forensic logistics to optimize information flows and build the most effective analytical human and computer processing, not excluding the use of artificial intelligence systems. Digital forensic logistics is a sub-branch of digital forensics in the collection, identification, storage, verification, and analysis of data, as well as the generation of electronic evidence for evidence in court. The article provides the main directions of digital forensic logistics, including the logistics of evidence in criminal cases; logistics of the general organization of crime investigation; logistics planning (selection of tools and methods of investigation); logistics of putting forward versions of events; logistics of decisions in criminal matters. It is argued that the efficiency of the entire system will largely depend on the establishment of information flows and the prioritization of tasks. Quality work requires the improvement of applied digital technologies capable of providing the necessary algorithms of the evidentiary process. The use of special software, including the use of artificial intelligence systems, is becoming increasingly relevant. The logistics of making decisions in criminal cases ideally represents an electronic assistant, endowed with artificial intelligence or in the form of a special computer program, capable, based on the determination of the forensic significance of the obtained digital information (electronic evidence), to offer the investigator solutions that can change the course of the investigation and transfer the entire information system in a new state.

Аннотация. В статье исследуются исторические предпосылки и научные основы теории криминалистического мышления, его соотношения с более изученными формами мышления. Представлены современные точки зрения на данный феномен. Криминалистическое мышление может рассматриваться дуалистично: как набор исходных предпосылок познавательной деятельности (статично); как комплекс мыслительных операций (в динамике). Предлагается система признаков, характеризующих криминалистическое мышление, включающих в себя системность, игровые и эвристические качества, целенаправленность, оперативность и открытость. Анализируется комплексный характер криминалистического мышления, выступающего одним из объединяющих свойств различных субъектов уголовного судопроизводства. Сформулировано авторское определение криминалистического мышления как совокупности личностных характеристик познающего субъекта и способов познавательной деятельности, позволяющих воспринимать и обрабатывать фрагментированную, неполную информацию в целях выявления и объяснения преступного события. Ключевые слова: криминалистическое мышление, правовое мышление, криминалистическое познание, личность следователя, субъект познания.

CRIMINAL LAW SCIENCESАннотация. Актуальной задачей криминалистики и смежных уголовно-процессуальных дисциплин является информационно-справочное обеспечение следственной деятельности, что предполагает в числе прочего исследование форм донесения результатов научных исследований до адресата -следователя или другого участника уголовного судопроизводства. Цель: формирование теоретической модели информирования следователя и дознавателя путем разработки и внедрения мобильных приложений со справочной информацией, сравнение подходов к структуре криминалистических знаний в России и иностранных государствах. Методы: эмпирические методы сравнения, моделирования, анкетирования. Результаты: в статье выявлены особенности российского подхода к изложению справочно-методической информации (от общего к частному, от большого к малому и т. д.). По мнению автора, мобильные справочники следует организовывать согласно хронологическому подходу: соответственно последовательностям действий адресата таких справочников. Определены основные положительные признаки современных мобильных справочников: мобильность, оперативность, возможность функционирования без подключения к сети Интернет, возможность синтеза разнородных источников.