On the prospects of using information technologies in forensic activities in the context of the digital transformation of the Russian Interior Ministry system

Bulbacheva Anna Aleksandrovna PhD in Law Senior Researcher at the Department of Planning and Control over Scientific Research of the Scientific and Research Center of the Academy of Management of the Ministry of Internal Affairs of the Russian Federation 125171, Russia, g. Moscow, ul. Zoi I Aleksandra, , 8 anit-b@mail.ru Other publications by this author

Introduction.

As noted in his speech by Police Lieutenant General V.V. Kazmin, Head of the Forensic Center of the Ministry of Internal Affairs By 2024, the Ministry of Internal Affairs of Russia is conducting 52 types of examinations. DNA analysis and digital technologies for collecting and processing information are now being actively used. 3D printing is being used to create three-dimensional models of the objects under study. Drones are actively used to inspect large areas, large facilities located in hard-to-reach places or in dangerous areas. Research methods adapt to new challenges and threats" ^[1].

The relevance of using IT in forensic activities is due to the need to increase the effectiveness of investigations, improve the quality of data analysis and quickly identify suspects. With the increasing volume of information and complexity of modern crimes, traditional investigative methods are becoming insufficiently effective, which underscores the importance of integrating innovative technologies into the practical activities of law enforcement agencies.

The purpose of this article is to analyze the current state and prospects of the use of information technology (hereinafter – IT) in forensic expertise.

To achieve this goal, it is necessary to solve the following tasks::

to study the existing IT technologies used in criminology;

analyze examples of successful use of IT in investigations;

to consider the possibilities and limitations of using IT in forensic activities;

to discuss the ethical and legal aspects of using artificial intelligence (AI) technologies.

The research methodology is based on the analysis of the works of Russian authors, as well as on the study of legislation and regulatory documents.

The following methods were used in the course of the research: scientific cognition, comparison, analysis, synthesis, induction, deduction, generalization.

The subject of the research is the criminalistic potential of using information technologies in the field of expert and criminalistic activities.

Such scientists as F.G. Aminev ^[2], A.A. Bessonov ^[3], Yu.V. Gavrilin ^[4], A.M. Kustov ^[5], N.P. Mailis ^[6], E.R. Rossinskaya ^[7], and others are engaged in research in this field. Despite the fact that the scientific development of this topic is evident in a number of studies on the use of machine learning, big data processing and neural networks in criminology, questions remain regarding ethical aspects, the reliability of algorithms and their impact on the decision-making process.

The main part.

The Ministry of Internal Affairs' Departmental Digital Transformation Program The strategy of the Russian Federation for 2022-2024, approved by Order of the Minister of Internal Affairs of the Russian Federation No. 1/37 dated January 11, 2022, is aimed at creating a digital infrastructure and providing information and analytical support for law enforcement agencies, making significant adjustments to the activities of law enforcement agencies, including forensic activities, creating the need to use methods of working with "large data", as well as various information and analytical systems and decision support complexes.

The introduction of digital technologies in forensic science has been going on for several decades. Modern activities of investigative bodies, forensic units and operational services would not be possible without these technologies, as well as without the relevant knowledge, skills and abilities of their application ^{[8, p. 103]}.

Automated information retrieval systems (hereinafter referred to as AIPS) are modern tools for effective information analysis and processing ^{[9, p. 89]}.

Papilon ADIS is designed for multibiometric identification, it provides automatic processing and comparison of incoming handprints from crime scenes.

For example, ADIS Papilon-9 and Neuroexpert software supplied new software for ADIS to the Ministry of Internal Affairs of Russia in the Republic of Crimea at the end of 2022. ^{[10, p. 15]}.

In February 2023, the specialists of the ECC of the Ministry of Internal Affairs of the Russian Federation for the Republic of Crimea summed up the results of the first one and a half months of regular operation of the Papilon-ADIS-9-Neuroexpert software. For 500 handprints entered into the ADIS database during this time: experts performed a routine review of ~7,500 candidates in visible recommendation lists (~15 candidates per trace) and identified 136 "footprint-fingerprint" matches; the neural network performed an analysis of visible recommendation lists and search histories and selected for manual review (marked with a neuroindex) 327 "footprint-fingerprint" pairs ^{[10, p. 16]}.

The experts reviewed the modified recommendation lists generated by the neural network and identified 141 "footprint-fingerprint" matches, while: for one "native" candidate, on average, there were ~1.3 "strangers", there were no omissions of "native" candidates in the visible parts of the lists, an additional five identifications (+~3.7%) were found Using a neural network in search histories outside the visible parts of the lists, the labor costs of experts to view "other people's" candidates for the DAC decreased by almost 40 times compared to viewing recommendation lists in normal mode ^{[10, p. 16]}.

The first results of the regular operation of the Neuroexpert software in the Ministry of Internal Affairs of Russia in the Republic of Crimea showed the possibility of a 40-fold reduction in the labor costs of experts to view "other people's" candidates with a simultaneous increase in efficiency by 3.7% due to additional identifications from search histories ^{[10, p. 17]}.

IPS "TRACE" includes three sections on different qualifications of traces from crime scenes: "SHOES", "TIRES", "HACKING", which allow you to register and analyze the data obtained during the investigation;

AIPS "Portrait" is designed to create subjective portraits of crime suspects based on the collected data. It helps in identifying suspects by various criteria, which significantly speeds up the investigation process.;

The Arsenal AIPS is designed to identify firearms, it allows you to create electronic bullet libraries with a volume of tens and hundreds of thousands of objects and bring to a qualitatively new level the performance of tracological examinations of fired bullets, their fragments and spent cartridges in the investigation of crimes related to the use of firearms ^{[11, p. 90]}.

The software and hardware complexes "Wildis" and "Deviza-M" were created for the technical examination of documents (passports, driver's licenses, banknotes, securities, etc.), which make it possible to detect signs of document forgery using various lighting conditions of both the visible and invisible radiation spectrum ^{[11, p. 91]}.

Visosoft software is used to analyze and process visual information, including photographs and videos, which helps in establishing facts and evidence.

The Mobile Criminalist software package developed by Oxygen Software is designed to work on mobile devices and allows experts to analyze data and access information right at the scene of an incident, which significantly speeds up the decision-making process ^{[11, p. 92]}.

Each of these systems plays an important role in the crime investigation process, enabling law enforcement agencies to more effectively collect, analyze, and use information to solve crimes and ensure security. The next stage in the development of information technology in forensic science is the use of AI and artificial neural networks.

At the legislative level, the concept is formulated in Decree of the President of the Russian Federation No. 490 dated October 10, 2019 "On the development of artificial Intelligence in the Russian Federation", according to which "artificial intelligence" is a complex of technological solutions that allows simulating human cognitive functions (including self-learning and finding solutions without a predefined algorithm) and obtaining results when performing specific tasks. comparable, at least, with the results of human intellectual activity."

In order to form a forensic concept regarding this concept of AI, we will consider a number of definitions proposed by various researchers.

Bolotova L.S. defines AI as a computer system capable of carrying out the processes of obtaining, processing and storing information and knowledge, as well as performing various operations with them, which together can be considered as a form of thinking ^{[12, p. 245]}.

In the work of Sinelnikova V.N. it is indicated that AI is a computer program created by humans and having the ability to create new information in accordance with the command architecture embedded in it ^{[13, p. 20]}.

A similar point of view is shared by A.A. Shchitova, who understands AI as a program with such a level of intelligence that it is able to recognize itself and make independent decisions ^{[14, p. 96]}.

According to Gavrilin A.V., Filatov A.A. "AI is the ability of a certain technological unit (such as a computer program, robot or system) to perform relatively autonomous intellectual and creative functions and tasks characteristic of humans. In addition, he is able to formulate conclusions and carry out actions based on available data" ^{[4, p. 128]}.

Kustov A.M. notes that: "AI is a complex of technological solutions that allow simulating human cognitive functions (including searching for solutions without a predefined algorithm) and obtaining results comparable to or superior to the results of human intellectual activity when performing specific tasks" ^{[5, p. 125]}.

So Rossinskaya E.R. believes that AI is currently perceived as a technology that is capable of self-learning based on data analysis, making autonomous decisions from those provided for during its creation (weak AI) or actually developed (strong AI) and simulating the results of human activity, including in the framework of communication and art ^{[7, p. 21]}.

A.A. Bessonov suggests considering AI in two aspects: "As an extension of sources of criminalistically significant information about criminal acts being prepared, committed and committed. As technologies that can and should be integrated into the work of investigative bodies as innovative tools for organizing investigations and searching for evidentiary and other data" ^{[3, p. 24]}.

In view of the above, the position is interesting Amineva F.G., "AI should be analyzed in two aspects: firstly, as an effective technical tool and an intellectual assistant for solving various, mostly routine, expert tasks; secondly, as an object for expert research" ^{[2, p. 9]}.

The analysis of scientific sources led to the conclusion that AI, in relation to forensic expertise, is a set of computer technologies and algorithms capable of analyzing, processing and interpreting data, as well as automating processes related to the investigation of crimes and the conduct of expertise. It includes machine learning, natural language processing, and big data analysis methods, which allows law enforcement agencies to increase work efficiency, improve the quality of investigations, identify patterns and predict possible crimes, and provide a more accurate and objective assessment of case materials.

AI, in the course of its training and self-learning, demonstrates impressive abilities to process and analyze large amounts of data, which are often beyond the control of humans. It is able to identify patterns and establish connections between different objects, which is extremely important for solving complex problems. This approach makes it possible to significantly speed up the identification and research processes, even in the absence of obvious traces or objects for analysis. In addition, the use of AI technologies minimizes the influence of the subjective factor, which makes the results more accurate and reliable.

Modern approaches to the application of information technology in forensic science are developing in accordance with the new challenges and opportunities offered by the digital age. Here are a few key directions:

Big Data Analysis: The use of big data processing and analysis technologies allows experts to collect and process huge amounts of information from various sources. This can include data from social media, phone conversations, and transactions, which helps identify patterns and connections that can be useful in investigations.;

Machine learning and AI: These technologies are used to automate data analysis and pattern recognition processes. For example, machine learning can be used to analyze surveillance videos, recognize faces, and predict probable crimes based on historical data.;

Digital Forensics: These are new tools and techniques for extracting and analyzing digital evidence, such as data from mobile devices, computers, and cloud storage, which include the use of software to recover deleted data and analyze network activity.;

Virtual and augmented reality: These technologies can be used to create 3D models of crime scenes or to train law enforcement officers. This allows you to better visualize situations and conduct effective training.;

Blockchain: The use of blockchain technologies to ensure the integrity and security of data can be useful in a situation where it is necessary to preserve evidence and ensure the confidentiality of data transmission.;

Information systems and databases: The development of integrated information systems that combine data from various sources (for example, databases of offenders, lost items, etc.), allows law enforcement agencies to manage information more effectively and respond quickly to incidents.;

Social Media Analysis: Using specialized tools to monitor social media and analyze interactions between users can help identify potential threats and analyze the behavior of suspects. These approaches not only increase the effectiveness of forensic expertise, but also contribute to a deeper understanding of crime and its prevention methods.

The use of information technologies in forensic science leads to the emergence of new patterns of public relations, which can be distinguished in several key aspects.:

Simplify access to information: Modern information technologies allow experts and criminologists to quickly access huge amounts of data. This improves the quality of the analysis and allows you to find the necessary information faster, which, in turn, increases the effectiveness of investigations.;

Networking: the use of information technology promotes closer cooperation between various structures – law enforcement agencies, forensic experts, scientific institutions and international organizations. This creates new forms of collaboration and allows the exchange of experience and data.;

anonymity and security: in the era of digitalization, there is a need to ensure the anonymity and security of participants in forensic activities. New technologies make it possible to protect data and personal information, which can change the nature of the relationship between experts and law enforcement agencies.;

Process automation: the introduction of automated data analysis and processing systems is changing traditional approaches to conducting expertise. This may lead to a change in the role of the expert, who becomes not only a data interpreter, but also an analyst.;

changing reporting formats: information technologies allow for the creation of more complex and interactive formats of reports and presentations, which can change the perception of examination results from both law enforcement agencies and judicial authorities;

Ethics and legal aspects: The use of information technology raises important issues of ethics and legal regulation. There is a need to develop new norms and standards related to the use, storage and protection of digital data.;

Training and professional development: the emergence of new technologies requires constant updating of knowledge and skills among employees, which affects the system of education and training of specialists in the field of criminology.;

Social responsibility: in the context of digitalization, the responsibility of experts for decisions is increasing, as automated systems can make mistakes. This creates new challenges for professional ethics and work standards.

Thus, the development of information technologies in forensic expertise creates new patterns that require adaptation by both the professional community and society as a whole.

To date, AI has demonstrated significant achievements, but it still has a number of drawbacks and limitations.

Here are some of them:

Lack of understanding of context: AI is often unable to interpret context correctly, which can lead to incorrect conclusions or recommendations. For example, language models may not understand subtle nuances of meaning or irony.;

Data dependence: The quality of an AI's work largely depends on the volume and quality of the data it is trained on. If the data is incomplete, distorted, or biased, it can lead to erroneous conclusions or discriminatory results.;

Lack of "common sense": AI systems do not have the intuition or common sense that humans have. They can make logical mistakes that seem absurd to humans.;

Difficulty of explanation: Many AI models, especially those based on deep neural networks, are "black boxes" - When processing multidimensional data, it becomes difficult to trace exactly how the input parameters interact with each other and affect the results, which makes it difficult to understand their decisions and results. This can cause distrust among users and make it difficult for them to integrate into critical areas.;

Ethical and legal issues: AI can be used for manipulative or unethical purposes, such as in surveillance systems or to spread disinformation. This raises questions about privacy, security, and responsibility.;

Vulnerability to attacks: AI systems can be vulnerable to various types of attacks, including adversarial attacks, in which small changes in input data can lead to incorrect conclusions.;

The need for human control: In most cases, successful AI applications require human intervention and control. Full automation in demanding fields such as medicine or law can be risky.;

Social and economic impacts: AI-based automation can lead to job losses in certain sectors, causing social and economic problems. Society must adapt to these changes.;

Interpretability issues: It is important that users can understand how and why the AI came to a certain conclusion.;

The lack of interpretability can complicate the decision-making process and the implementation of AI in professional practice. These shortcomings highlight the need for a careful and responsible approach to the development and implementation of AI technologies, as well as the importance of monitoring their impact on society.

Conclusion.

The prospects of using information technology in forensic expertise in the context of the digital transformation of the Russian Interior Ministry represent a significant step forward in ensuring the effectiveness and reliability of investigations. The introduction of modern digital solutions and tools not only optimizes the processes of data collection, analysis and storage, but also contributes to improving the quality of expert research and conclusions.

Nevertheless, for the successful implementation of these opportunities, it is necessary to overcome a number of challenges, such as the high cost of implementing these technologies, the need to adapt personnel and ensure data security. With these aspects in mind, a key factor will be an integrated approach to information technology integration, which should include both technical and organizational measures.

Thus, the active use of information technology in forensic activities can significantly improve the work of law enforcement agencies, make it more effective, which, in turn, will contribute to improving the level of security in society. It is important that the process of digital transformation is continuous and adapts to rapidly changing conditions, which will maximize the use of scientific and technical potential in the service of the law.

Further research in this area will help not only optimize investigative processes, but also develop reliable mechanisms to protect citizens' rights in the face of increasing digitalization.