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Police and Investigative Activity
Reference:

Forensic support for the use of artificial intelligence technology in the detection and investigation of crimes

Khamidullin Ruslan Sibagatullovich

PhD in Law

Senior Lecturer of the Criminalistics Department of the V.F. Yakovlev Ural State Law University

620072, Russia, Sverdlovsk region, Yekaterinburg, Syromolotov str., 7, sq. 11

sledgsugu@mail.ru
Other publications by this author
 

 

DOI:

10.25136/2409-7810.2024.1.68894

EDN:

CBMNNY

Received:

06-11-2023


Published:

16-09-2024


Abstract: The subject of the study is the possibility of using artificial intelligence systems in order to solve and investigate crimes. The author, using the example of GPT (Generative Pre-trained Transformer) neural network architecture, invented by Google researchers, shows the capabilities of artificial intelligence to analyze situations arising during the disclosure and investigation of crimes and to put forward reasonable versions based on them on the circumstances of the commission of an illegal act. The study demonstrates the possibility of drawing up programs (plans) for criminal investigations based on such versions. The object of the study is social relations arising during the use of artificial intelligence systems in law enforcement. Special attention is paid to the process of training the neural network to use modern forensic tools and methods in detection, disclosure and investigation. The main conclusions of the study are the need for training and the use of neural networks in law enforcement not only for the purpose of identifying, disclosing and investigating crimes and administrative offenses, but also their prevention and suppression. A special contribution of the author to the research of the topic is the identification of patterns that arise in the process of learning and using the capabilities of the neural network in providing assistance to law enforcement entities in applied research. The novelty of the work lies in the experiment conducted for the first time on training and using a separate GTP neural network in forensic analytical work on putting forward versions and planning an investigation. The paper provides recommendations for the training of law enforcement officers on the effective use of neural networks in law enforcement.


Keywords:

Forensic support, neural network, artificial intelligence, detection, disclosure, investigation, crimes, investigator, administrative offenses, operative

This article is automatically translated.

In the history of technology, there are many examples of how designers and inventors have tried to develop machines that will be able to facilitate individual mental operations, usually of a computational nature. Some of these inventions include W. Schickard's counting clock (1623), B. Pascal's summing machine (1642), G. F. Leibniz's arithmometer (1673), and the logic demonstrator Ch. Stanhope (1777) and the difference machine (the machine of differences) by C. Babbage (1822). Although these inventions were not intelligent computer systems, they had a significant impact on the development of computer technology and led to the emergence of such an important technology as artificial intelligence, which D.V. considered in more detail in his doctoral dissertation research. Bakhteev [1].

The use of artificial intelligence in the detection and investigation of crimes represents significant progress in the field of criminology. This technology makes it possible to change the traditional working methods of law enforcement agencies, enriching their arsenal of tools. The forensic aspects of the application of artificial intelligence include the analysis of large amounts of data, pattern recognition, automated processing of evidence and forecasting the commission of new crimes. E-digital learning allows you to create models that can identify patterns in criminal events, determine the characteristics of potential criminals and identify key factors affecting the dynamics of the situation in this context.

However, the ethical and legal aspects of the use of artificial intelligence in criminology should be taken into account, such as the transparency of algorithms, maintaining data confidentiality and preventing possible problems with decision-making. This paper will examine how the use of artificial intelligence affects the effectiveness and reliability of crime detection and investigation, as well as what challenges and prospects this process is subject to in the context of forensic activities.

Artificial intelligence helps to improve the quality of law enforcement agencies by automating routine operations and data analysis. Machine learning systems can quickly process huge amounts of information, revealing hidden connections between various events and persons, which significantly speeds up the investigation process [2].

According to the National Strategy for the Development of Artificial Intelligence for the period up to 2030 (Decree of the President of the Russian Federation dated 10.10.2019 No. 490 On the development of artificial Intelligence in the Russian Federation (together with the "National Strategy for the Development of Artificial Intelligence for the period up to 2030"), this means a set of technological solutions that allows simulating human cognitive functions (including self-learning and search for solutions without a predetermined algorithm) and obtain results comparable to at least the results of human intellectual activity when performing specific tasks. The complex of technological solutions includes information and communication infrastructure, software (including those that use machine learning methods), processes and services for data processing and solution search.

Important aspects that should be taken into account when using artificial intelligence systems are, first of all, ensuring the protection of constitutional human and civil rights and freedoms. This also applies to everyone's right to privacy. It is also necessary to emphasize the inadmissibility of using artificial intelligence to intentionally harm citizens and legal entities.

Another important aspect is the prevention and minimization of risks that may arise as a result of the use of artificial intelligence technologies. In this regard, it is important to organize the work of artificial intelligence algorithms in such a way that they are open and understandable. This will allow you to control the process of achieving results and prevent possible negative consequences [3].

In recent years, the works of A.A. Bessonov [4], V.F. Vasyukov [5], R.I. Dremlyugi [6], I.A. Zavyalov [7], A.M. Kustov [8], A.I. Shemetov [9] and D.V. Bakhteev [10] have investigated and reflected various aspects of the application artificial intelligence in the detection and investigation of crimes. These studies make a significant contribution to the development of the field of artificial intelligence and its application in the field of law enforcement.

In the light of the increased crime rate, the use by criminals of new methods and means of committing crimes, as well as insufficiently effective detection rates, it becomes necessary to search for new ways to improve activities related to the identification, disclosure and investigation of crimes. An insufficiently effective system of indicators, a low level of training of law enforcement officers, further bureaucratization of the investigative and judicial system, subordination of the subjects of investigation, corruption risks of criminal procedural activities and directly criminal procedural legislation and a number of other factors negatively affect the quality of this activity. The intellectual capabilities of individual law enforcement officers, investigators, operatives, prosecutors, as well as the heads of these structures play a key role in changing the current situation. However, this approach turns out to be insufficient for a radical change in the current situation. In this regard, a qualitatively new system of intellectual support for operational investigative, forensic and criminal procedural activities, including the use of artificial intelligence systems, becomes necessary. Artificial intelligence is an essential attribute in modern and effective operational investigative and criminal procedural activities, and not a factor leading to the exclusion of the human element. The system of intellectual support of the criminal process should include the following interrelated and complementary elements:

- training of employees involved in the identification, disclosure and investigation of crimes;

- legislative support of the criminal process;

- scientific and research support of criminal procedural activities;

- information and technical support of criminal proceedings;

- the use of artificial intelligence systems in criminal proceedings, criminology and operational investigative activities.

At the same time, the introduction of artificial intelligence systems opens up prospects for the successful development of criminal proceedings [11].

In our opinion, the most important element in the detection and investigation of crimes using artificial intelligence is the forensic support of such activities.

In the course of the study, we will not dwell on the definition of the concept and essence of forensic software in general. In the dissertation research conducted in 2018 on the topic: "Forensic support for the activities of an investigator ...", we considered in detail the issues of forensic support for the activities of an investigator in the disclosure and investigation of crimes. As a result, we came to the conclusion that the forensic support of the activities of the subjects of the disclosure and investigation of crimes is a complex of knowledge, skills and abilities, including the use of technical means, techniques and tactics in the disclosure of crime investigation [12].

It should be noted that in this study, the criminalistic provision of disclosure and investigation of a crime through the use of artificial intelligence should be understood as a set of knowledge and skills of subjects of disclosure and investigation of crimes on the effective use of artificial intelligence systems in solving official law enforcement tasks.

Currently, forensic support for the disclosure and investigation of crimes is becoming an increasingly urgent and difficult task for law enforcement agencies. The difficulty lies in the need to analyze large amounts of information, identify hidden connections and patterns, as well as predict possible scenarios for the development of the case. In this regard, the use of artificial intelligence (AI) can become an effective tool to improve the results of an investigation.

One example of the use of AI in criminology is the use of GPT (Generative Pre-trained Transformer). GPT is an artificial intelligence model that learns from large amounts of text data and is able to generate coherent answers to questions asked or materials provided.

Within the framework of the conducted research, GPT training was carried out on materials studied by students of the criminology course in higher educational institutions. With the help of the AI chat, tasks and information necessary to solve these tasks are provided. AI provided answers efficiently and accurately, offering hypotheses, compiling programs and algorithms of actions necessary for the detection and investigation of crimes at various stages.

To test the effectiveness of GPT in real conditions, we conducted an experiment using materials from a criminal case under investigation by the Main Investigative Department of the Ministry of Internal Affairs of Russia in the Sverdlovsk region. Using the GTP chat, case materials were provided, and based on this information, the AI put forward versions and developed an investigation program at the initial stage. After that, the proposed actions and activities of GPT are compared with the actions carried out by investigators and operatives in the framework of a real investigation.

The results of the experiment showed that the artificial intelligence presented by GPT is able to analyze in detail and objectively and plan the activities necessary for the detection and investigation of crimes. It can also help in the formation of examination questions and the definition of various types of examinations. This allows us to conclude that it is possible to effectively use artificial intelligence in law enforcement not only to solve crimes, but also to prevent their commission, determine the circumstances and the crime scene.

However, it should be noted that the use of artificial intelligence in forensic software has its limitations. Firstly, AI does not have human intuitive understanding and emotional experience, which may affect the accuracy of its conclusions. Secondly, AI can be error-prone, especially if the training was conducted on incomplete or incorrect data.

Nevertheless, if applied correctly, artificial intelligence can become a valuable tool for criminalistically ensuring the detection and investigation of crimes. It can significantly speed up the process of analyzing information and organize the planning of an investigation at its various stages. As a result, the use of artificial intelligence can improve the efficiency of law enforcement agencies and improve the quality of crime investigation.

Before revealing the essence of one of the experiments conducted with GTP, let's consider the essence of this artificial intelligence. The history of GPT (Generative Pre-trained Transformer) has its roots in research conducted at OpenAI, an artificial intelligence laboratory, in order to create a powerful and versatile model for processing and generating natural language. When conceiving GPT, the researchers sought to create a model that could generate high-quality and natural text based on the studied samples of text data.

Let's also say that artificial intelligence is a general concept, and a neural network is a specific implementation of artificial intelligence. The first model, GPT-1, was introduced in 2018 and showed promising results in text generation. GPT-1 worked on the principle of pre-training and precise adjustment to specific tasks. This model used Transformer technology, which is a neural network with multiple layers of attention, capable of processing context and connections within text, improving the quality of generation.

In 2019, the GPT-2 model was introduced, which optimized previous results, expanding the power and capabilities of text generation. GPT-2 was trained on a huge amount of data, including online articles, books, news and other texts. Due to its unique architecture and multi-level processing, GPT-2 was able to generate long and high-quality texts, as well as overcome semantics-related tasks such as questions and answers.

After that, in 2020, the GPT-3 model was introduced, which is considered the most advanced and scalable version. GPT-3 is a huge neural network containing billions of parameters, which allows it to generate texts with amazing accuracy and stylistic naturalness. It has a wide range of applications, including automatic content creation, answering questions, translating text, developing dialog systems, and more.

As a result of numerous studies and technological improvements, GPT models have become a sought-after and successful technology in the field of natural language processing and generated text. OpenAI continues to develop and improve its models, striving to create even more sophisticated and efficient language processing systems based on artificial intelligence.

The analysis of the functionality of artificial intelligence, in this case, the GTP chat, allows us to conclude that it can be used in the process of putting forward versions in the detection and investigation of crimes. GTP, thanks to its software equipment and analytical capabilities, is able to take into account the investigative situation that arises at various stages of the investigation, as well as based on the achievements of the science of criminology, information about the presence of suspects or suspected persons and their characterizing materials to put forward reasonable versions about the person who committed the crime.

It is important to note that the use of artificial intelligence, especially GTP, in the forensic support of the process of disclosure and investigation of crimes has not been previously studied, which is the scientific novelty of this work.

Artificial intelligence, unlike human thinking, is not inclined to miss or distort the factual circumstances that have been provided to it, and also uses a variety of forensic investigative practices accumulated over many years to develop patterns and detect trends in the detection and investigation of crimes. When putting forward a version, artificial intelligence excludes subjective factors and objectively takes into account all circumstances, which contributes to the reliability of its conclusions.

Here is an example of the successful use of AI in criminology, in an attempt to verify how much artificial intelligence, in this case GTP, is able to correctly and objectively put forward versions regarding the perpetrators of a crime based on the data provided. The hypothesis was that artificial intelligence, specially designed to solve forensic tasks like our investigative exercises, is able to offer versions based on established patterns.

In this study, training and interaction with GTP takes place in Telegram via chat. All training materials are provided through this chat, and GTP tasks are set through it based on the information provided so that it can put forward versions and make descriptive fragments of protocols. This makes it possible to effectively use GTP in criminology and increases its capabilities in investigating and countering crimes.

For this purpose, we will demonstrate a dialogue in Telegrams between the author and the artificial intelligence GTP (Fig. 1):

Author: Have you learned how to put forward theories?

GPT + Midjourney: Yes, I can submit different versions or proposals according to the given context or requirements.

Author: Put forward a version of who could have committed the crime in accordance with the given context: "At about 3.00 o'clock Andreev woke up to the sound of the alarm of his car VAZ 21073. Andreev looked out the window and saw three young men running from his car, one of whom was carrying a backpack on his back. When Andreev went out to the parking lot, he noticed that the driver's window of his car was broken, a car radio, a phone holder, and a DVR were stolen from the cabin. Andreev did not notice any special signs of criminals, since it was dark."

GPT + Midjourney:

Version 1: According to the given context, it is likely that the three young men whom Andreev saw may be suspects in the commission of a crime. One of them was carrying a backpack on his back, which may indicate that he may have been responsible for stealing items from the car. However, due to the darkness, Andreev could not notice the special signs of the criminals, so a more accurate description of the identity of the suspects is not available.

Version 2: Based on the information, the criminal committed theft from Andreev's car. It is likely that the young people he saw could have been involved in this crime. The broken driver's window and stolen items such as a car radio, a phone holder and a DVR indicate that the crime was targeted. However, due to the darkness, Andreev could not notice the special signs of the criminals, complicating the identification of suspects.

Fig. 1.

During the research, artificial intelligence was trained based on GPT, the capabilities of AI were noted to analyze forensic tasks and develop their solutions in the same way as students studying a course in criminology do. AI was provided with tasks and materials that can be used to solve these tasks. GPT was effectively and accurately deployed, putting forward hypotheses, compiling programs and algorithms for the detection and investigation of crimes at different stages, depending on the progress of the investigation.

As part of this study, the possibility of using an artificial intelligence neural network to write a fragment of the descriptive part of the object inspection protocol was tested. To do this, the neural network was provided with detailed forensic photography of the knife, on the basis of which artificial intelligence compiled a fragment of the description of the object. Then the investigator used this fragment to compile a complete protocol, indicating the features of the examined knife (Fig. 2, 3).

Figure 2.

Fig.3

In addition, after training the neural network, she was given the opportunity to compile a fragment of the descriptive part of the document inspection protocol with signs of forgery. It is important to note that artificial intelligence drew attention to these signs and described them.

Currently, an experiment is being conducted in cooperation with the Main Investigative Department for the Sverdlovsk region. Within the framework of which the capabilities of artificial intelligence, namely GPT, are used to uncover real crimes, criminal cases for which are under investigation by investigators. The artificial intelligence was provided with materials of the criminal case, on the basis of which it was asked to put forward versions and develop a program for investigating the crime at the initial stage. Then we compared the proposed actions of the GPT chat with the actions carried out as part of the investigation, and found that artificial intelligence analyzes and plans activities necessary for the disclosure and investigation of crimes in sufficient detail and objectively. It can also help in the formation of examination questions and the definition of various types of examinations. Based on this, we can conclude that artificial intelligence can be effectively used in law enforcement not only to solve crimes, but also to prevent their commission, determine the circumstances and the crime scene. The results of the experiment will be described in more detail after the consideration of criminal cases in court and the pronouncement of sentences that have entered into force.

Also, during the study, the possibility of using a neural network after its training was tested in order to compile a fragment of the descriptive part of the incident scene inspection protocol based on orientation and survey photographs. AI coped with this task much better than most students studying the course of criminology.

In order for artificial intelligence to effectively describe objects and documents, as well as signs of forgery and features of the subject, samples of the descriptive part of protocols from real criminal cases were initially uploaded to it. After that, the AI was able to compile a descriptive part of the document inspection protocol, where it described the document in detail, as well as the signs of forgery.

Another experiment was to train GTP artificial intelligence to describe injuries based on forensic medical literature, in particular the Atlas of Forensic Medicine edited by Yu.And Pigolkin. Based on this atlas, GTP provided nodal and detailed images of corpses with various injuries, such as gunshot, knife, stabbed, cut, etc. Then the GTP was given the task to compile a descriptive part of the protocol of examination of the corpse based on photographs taken at the scene of the actual incident. The descriptive part of the protocol compiled by GPT was no worse than the descriptive part of the protocol compiled by the investigator, and in some details it was more detailed.

It is worth noting that similar issues have been raised by foreign authors. In most cases, without analyzing to one degree or another the use of artificial intelligence in criminalistic support for the disclosure and investigation of crimes, research is mainly related to analytical work in preparation for investigative actions or the investigation of certain types and categories of crimes [13, 14, 15, 16, 17, 18, 19, 20]

Criminology, as a science, was originally created to use the achievements of other branches of science and technology, such as mathematics, physics, chemistry, biology, medicine and computer science, for the suppression, detection, disclosure, investigation and prevention of crimes. In this regard, criminology should actively use advances in the field of artificial intelligence systems to solve problems of effective crime prevention.

As a conclusion, it is worth noting that artificial intelligence is able to provide significant assistance to law enforcement entities, but not replace them. The possibilities of using artificial intelligence systems for the purpose of solving and investigating crimes have not been determined at the moment. Neural network architectures effectively demonstrate the capabilities of artificial intelligence to analyze situations that arise during the disclosure and investigation of crimes and to put forward reasonable versions based on them on the circumstances of the commission of an illegal act. It is also necessary that special attention should be paid to high-quality training of the neural network on the use of modern forensic tools and methods in detection, disclosure and investigation, only after that it can be applied in real life.

References
1. Bakhteev, D. V. (2020). Artificial intelligence in investigative activities: tasks and problems. Russian investigator, 9, 3-6.
2. Tarasov, A. V. & Temzokov, A. V. (2023). Forensic aspects of the use of artificial intelligence in solving and investigating crimes. Theory and practice of social development, 10, 256-261.
3. Kolycheva, A. N. (2022). Prospects for the introduction of artificial intelligence in the detection and investigation of crimes. Scientific Bulletin of the Oryol Law Institute of the Ministry of Internal Affairs of Russia named after V.V. Lukyanova, 3, 172-177.
4. Bessonova, A. A. (2021). Artificial intelligence and mathematical statistics in the forensic study of crimes. Moscow, Russia: Prospekt.
5. Vasyukov, V. F. & Shemetov, A. K. (2022). Possibilities of artificial intelligence in solving crimes. Bulletin of the Academy of the Investigative Committee of the Russian Federation, 1, 82-87.
6. Dremlyuga, R. I. (2019). Artificial intelligence systems in crime investigation. Information law, 1, 21-25.
7. Zavyalov, I. A. (2021). Foreign experience in using artificial intelligence in solving crimes. Bulletin of the Moscow University of the Ministry of Internal Affairs of Russia, 3, 228-236.
8. Kustov, A. M. (2021). Artificial intelligence in the investigation of grave and especially grave crimes. Theory and practice of crime investigation, 1, 16-25.
9. Andreev, V. K. (2020). Dynamics of legal regulation of the use of artificial intelligence. Journal of Russian Law, 3, 58-68.
10. Bakhteev, D. V. (2022). Pre-expert verification of signatures using the NSP dataset. The vth khmyrovsky criminalistic readings, 1, 115-122.
11. Afanasyev, A. Yu. (2018). Artificial intelligence or the intelligence of subjects identifying, solving and investigating crimes: which will win? Criminologist Library, 3, 28-34.
12. Khamidullin, R. S. (2023). Forensic support for the detection and investigation of crimes under Art. 210.1 of the Criminal Code of the Russian Federation “Occupation of the highest position in the criminal hierarchy”. Security questions, 3, 63-70.
13. Hasson, U. (2020). Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks. Neuron, 3, 416-434.
14. Kriegeskorte, N. (2018). Cognitive computational neuroscience. Nature Neuroscience, 21, 1148-1160.
15. Williams, R. M. (2019). Optical Illusions Images Dataset. INSAM Journal of Contemporary Music, Art and Technology, 2, 127-139.
16. Tshitoyan, V. (2019). Unsupervised word embeddings capture latent knowledge from materials science literature. Nature, 571, 95-98.
17. Ullman, S. (2019). Using neuroscience to develop artificial intelligence. Science, 363, 692-693.
18. Porter, M. D. (2016). A Statistical Approach to Crime Linkage. The American Statistician, 70, 152-165.
19. Pogarsky, G. (2017). Heuristics and biases, rational choice, and sanction perceptions. Criminology, 55, 85-111.
20. Pakkanen, T. (2012). The Effects of Coding Bias on Estimates of Behavioral Similarity in Crime Linking Research of Homicides. Journal of Investigative Psychology and Offender Profiling, 9, 223-234.

First Peer Review

Peer reviewers' evaluations remain confidential and are not disclosed to the public. Only external reviews, authorized for publication by the article's author(s), are made public. Typically, these final reviews are conducted after the manuscript's revision. Adhering to our double-blind review policy, the reviewer's identity is kept confidential.
The list of publisher reviewers can be found here.

The subject of the research in the article submitted for review is, as its name implies, the problem of criminalistic support for the use of artificial intelligence technology in the detection and investigation of crimes. The stated boundaries of the study are fully respected by the author. The methodology of the research is not disclosed in the text of the article, but it is obvious that the scientists used universal dialectical, logical, historical, formal legal, comparative legal research methods, methods of legal modeling and legal forecasting. The relevance of the research topic chosen by the author is undeniable and justified as follows: "In the history of technology, one can find many examples of how designers and inventors tried to develop machines that would be able to facilitate individual mental operations, usually of a computational nature. Some of these inventions include W. Schickard's counting clock (1623), B. Pascal's summing machine (1642), G. F. Leibniz's arithmometer (1673), and the logic demonstrator Ch. Stanhope (1777) and the difference machine (the machine of differences) by C. Babbage (1822). Although these inventions were not intelligent computer systems, they had a significant impact on the development of computer technology and led to the emergence of such an important technology as artificial intelligence, which D.V. considered in more detail in his doctoral dissertation research. Bakhteev [1]. The use of artificial intelligence in the detection and investigation of crimes represents significant progress in the field of criminology. This technology makes it possible to change the traditional working methods of law enforcement agencies, enriching their arsenal of tools. The forensic aspects of the application of artificial intelligence include the analysis of large amounts of data, pattern recognition, automated processing of evidence and forecasting the commission of new crimes. E-digital learning allows you to create models that can identify patterns in criminal events, determine the characteristics of potential criminals and identify key factors affecting the dynamics of the situation in this context. However, the ethical and legal aspects of the use of artificial intelligence in criminology should be taken into account, such as the transparency of algorithms, maintaining data confidentiality and preventing possible problems with decision-making." The degree of study of the problems raised in the article is reflected in the provisions of the main part of the article: "In recent years, in the works of A.A. Bessonov [4], V.F. Vasyukov [5], R.I. Dremlyugi [6], I.A. Zavyalov [7], A.M. Kustov [8], A.I. Shemetov [9] and D.V. Bakhteeva [10] investigated and reflected various aspects of the use of artificial intelligence in the detection and investigation of crimes. These studies make a significant contribution to the development of the field of artificial intelligence and its application in the field of law enforcement." The scientific novelty of the work is manifested in a number of conclusions of the author: "... a qualitatively new system of intellectual support for operational investigative, forensic and criminal procedural activities, including the use of artificial intelligence systems, becomes necessary. Artificial intelligence is an essential attribute in modern and effective operational investigative and criminal procedural activities, and not a factor leading to the exclusion of the human element"; "The results of the experiment showed that artificial intelligence, represented by GPT, is able to analyze in detail and objectively and plan activities necessary for the disclosure and investigation of crimes. It can also help in the formation of examination questions and the definition of various types of examinations. This allows us to conclude that it is possible to effectively use artificial intelligence in law enforcement not only to solve crimes, but also to prevent their commission, determine the circumstances and the crime scene. However, it should be noted that the use of artificial intelligence in forensic software has its limitations. Firstly, AI does not have human intuitive understanding and emotional experience, which may affect the accuracy of its conclusions. Secondly, AI can be prone to errors, especially if the training was conducted on incomplete or incorrect data," etc. Thus, the article makes a certain contribution to the development of domestic legal science and, of course, deserves the attention of potential readers. The scientific style of the research is fully sustained by the author. The structure of the work is quite logical. In the introductory part of the article, the scientist substantiates the relevance of his chosen research topic. In the main part of the work, the author analyzes how the use of artificial intelligence affects the effectiveness of crime detection and investigation, and also determines the prospects for its existence in the context of forensic activities. The final part of the article contains conclusions based on the results of the study. The content of the article fully corresponds to its title and does not cause any particular complaints, however, there are shortcomings of a formal nature in the work. So, there are typos in the article: "In light of the increased crime rate, the use of new methods and means of committing crimes by criminals, as well as insufficiently effective detection rates, it becomes necessary to search for new ways to improve activities related to the identification, disclosure and investigation of crimes" - the proposal has not been agreed. The author writes: "In the course of the study, we will not dwell on the definition of the concept and essence of forensic support in general" - more correctly, "In the course of the study, we will not dwell on the definition of the concept of "forensic support" and its essence in general." The scientist notes: "As a result, we came to the conclusion that the forensic support of the activities of subjects of disclosure and investigation of crimes is a complex of knowledge, skills and abilities, including the use of technical means, techniques and tactics in the disclosure of crime investigation [12]" - "in disclosure and investigation". The author points out: "We note that in this study, criminalistic support for the disclosure and investigation of a crime through the use of artificial intelligence should be understood as a set of knowledge and skills of subjects of disclosure and investigation of crimes on the effective use of artificial intelligence systems in solving official tasks of law enforcement" - "disclosure and investigation of crimes". The scientist writes: "GTP, thanks to its software equipment and analytical capabilities, is able to take into account the investigative situation that arises at various stages of the investigation, as well as based on information about the presence of suspects or suspected persons and their characterizing materials, advances in the science of criminology to put forward reasonable versions about the person who committed the crime" - a comma is omitted after the word "person"; "committed", not "committed". The bibliography of the study is presented by 20 sources (monograph and scientific articles). From a formal and factual point of view, this is quite enough. The nature and number of sources used in writing the article allowed the author to reveal the research topic with the necessary depth and completeness. There is an appeal to the opponents, but it is general in nature due to the focus of the study. The scientific discussion is conducted by the author correctly; the provisions of the work are justified to the necessary extent and illustrated with examples.
There are conclusions based on the results of the study ("As a conclusion, it is worth noting that artificial intelligence is able to provide significant assistance to law enforcement entities, but not replace them. The possibilities of using artificial intelligence systems in order to solve and investigate crimes have not been determined at the moment. Neural network architectures effectively demonstrate the capabilities of artificial intelligence to analyze situations that arise during the disclosure and investigation of crimes and to put forward reasonable versions based on them on the circumstances of the commission of an illegal act. It is also necessary that special attention should be paid to high-quality training of neural networks on the use of modern forensic tools and methods in identification, disclosure and investigation, only after that it can be applied in real activity"), have the properties of reliability and validity and undoubtedly deserve the attention of potential readers. However, they need additional subtraction due to the typos encountered ("mark", not "mark"; "It is also necessary that special attention should be paid ..." - there is lexical redundancy; "... high-quality training of the neural network on the use of modern forensic tools and methods in identification, disclosure and investigation" - the word is omitted "crimes"). The interest of the readership in the article submitted for review can be shown primarily by specialists in the field of criminology, provided that it is slightly improved: disclosure of the research methodology and elimination of violations in the design of the work (typos).

Second Peer Review

Peer reviewers' evaluations remain confidential and are not disclosed to the public. Only external reviews, authorized for publication by the article's author(s), are made public. Typically, these final reviews are conducted after the manuscript's revision. Adhering to our double-blind review policy, the reviewer's identity is kept confidential.
The list of publisher reviewers can be found here.

The subject of the study. In the peer-reviewed article "Forensic support for the use of artificial intelligence technology in the detection and investigation of crimes", the subject of the study is the norms of law governing public relations on the use of artificial intelligence in the detection and investigation of crimes. A special place in the subject of research is occupied by the study of the admissibility, expediency and effectiveness of digital technologies in general, and artificial intelligence, in particular, in the disclosure and investigation of socially dangerous acts. Research methodology. When writing the article, such methods were used as: logical, historical, theoretical and predictive, formal legal, system-structural and legal modeling. The methodological apparatus consists of the following dialectical techniques and methods of scientific cognition: analysis, abstraction, induction, deduction, hypothesis, analogy, synthesis, typology, classification, systematization and generalization. The work used a combination of empirical and theoretical information. The use of modern methods made it possible to study the established approaches, views on the subject of research, develop an author's position and argue it. The relevance of research. The relevance of the research topic stated by the author is beyond doubt. The global digitalization of all spheres of life creates certain "convenience" for specialists in various fields of knowledge, which in turn requires appropriate legal regulation. The author rightly notes that "... artificial intelligence contributes to improving the quality of law enforcement agencies by automating routine operations and data analysis." These circumstances indicate the relevance of doctrinal developments on this topic in order to improve legislation and practice of its application. Scientific novelty. Without questioning the importance of previous scientific research, which served as the theoretical basis for this work, nevertheless, it can be noted that this article for the first time formulated noteworthy provisions, for example: "...artificial intelligence is able to provide significant assistance to law enforcement entities, but not replace them. The possibilities of using artificial intelligence systems for the purpose of solving and investigating crimes have not been determined at the moment. Neural network architectures effectively demonstrate the capabilities of artificial intelligence to analyze situations that arise during the disclosure and investigation of crimes and to put forward reasonable versions based on them on the circumstances of the commission of an illegal act." Based on the results of writing the article, the author has made a number of theoretical conclusions and suggestions, which indicates not only the importance of this study for legal science, but also determines its practical significance. Style, structure, content. The article is written in a scientific style, using special legal terminology. However, the text of the article is not free of errors and typos: for example, "it is worth noting", "Atlas of Forensic Medicine", etc. It is also noted that the rules of punctuation in sentences are not observed: for example, "the protocol drawn up by the investigator", etc. There are repetitions of words in sentences: for example, "In this regard, criminology should actively use advances in the field of artificial intelligence systems to achieve the goal of effectively countering crime." In addition, the author's position on the terms "artificial intelligence" and "neural network" is unclear: are the categories identical or not? In general, the material is presented consistently and clearly. However, the text of the article was not carefully read by the author. For clarity and ease of perception of the material, the theoretical provisions are illustrated with figures (however, the numbering is broken, Figure 2 is missing). The article is structured. It seems that the introduction does not fully meet the requirements for this part of the scientific article. And also, in conclusion, it would be necessary to formulate the main results that the author achieved during the research. The comments are disposable. The topic is disclosed, the content of the article corresponds to its title. Bibliography. The author uses a sufficient number of doctrinal sources, provides links to publications of recent years. References to sources are designed in accordance with the requirements of the bibliographic GOST, with the exception of the source under No. 10. Appeal to opponents. A scientific discussion is presented on certain issues of the stated topic, and appeals to opponents are correct. All borrowings are decorated with links to the author and the source of the publication. Conclusions, the interest of the readership. The article "Forensic support for the use of artificial intelligence technology in the detection and investigation of crimes" is recommended for publication with the condition of its completion. The article corresponds to the topic and editorial policy of the journal "Security Issues". The article is written on an urgent topic, has practical significance and is characterized by scientific novelty. This article may be of interest to a wide readership, primarily specialists in the field of information law, criminal law, criminal procedure law and criminalistics, and will also be useful for teachers and students of law schools and faculties.

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A REVIEW of an article on the topic "Forensic support for the use of artificial intelligence technology in the detection and investigation of crimes." The subject of the study. The article proposed for review is devoted to topical issues of the use of artificial intelligence technology in the detection and investigation of crimes. The article examines various theoretical and practical issues that arise for lawyers, criminologists, and investigators regarding the use of modern digital technologies for forensic purposes. Empirical data and opinions of scientists were primarily used as a specific subject of research. Research methodology. The purpose of the study is not stated directly in the article. At the same time, it can be clearly understood from the title and content of the work. The purpose can be designated as the consideration and resolution of certain problematic aspects of the issue of criminalistic support for the use of artificial intelligence technology in the detection and investigation of crimes. Based on the set goals and objectives, the author has chosen the methodological basis of the study. In particular, the author uses a set of general scientific methods of cognition: analysis, synthesis, analogy, deduction, induction, and others. In particular, the methods of analysis and synthesis made it possible to summarize and share the conclusions of various scientific approaches to the proposed topic, as well as draw specific conclusions from examples of the use of modern technologies. Empirical methods played the greatest role. In particular, the author actively applied the formal legal method, which allowed an analysis of practical situations of using artificial intelligence technology in the detection and investigation of crimes. For example, the following conclusion of the author: "During the research, artificial intelligence was trained on the basis of GPT, the capabilities of AI were noted to analyze forensic tasks and develop their solutions in the same way as students studying a course of criminology do. AI was provided with tasks and materials that can be used to solve these tasks. GPT was effectively and accurately deployed, putting forward hypotheses, compiling programs and algorithms for solving and investigating crimes at different stages, depending on the progress of the investigation. As part of this study, the possibility of using an artificial intelligence neural network to write a fragment of the descriptive part of the object inspection protocol was tested. To do this, the neural network was provided with detailed forensic photography of the knife, on the basis of which artificial intelligence compiled a fragment of the description of the object. Then the investigator used this fragment to compile a complete protocol, indicating the features of the knife being examined." Thus, the methodology chosen by the author is fully adequate to the purpose of the study, allows you to study all aspects of the topic in its entirety. Relevance. The relevance of the stated issues is beyond doubt. There are both theoretical and practical aspects of the significance of the proposed topic. From the point of view of theory, the topic of ensuring the use of artificial intelligence technology in the detection and investigation of crimes is complex and ambiguous. Digital technologies can be useful for forensic purposes, but they pose new challenges, for example, in terms of personnel training. It is difficult to argue with the author that "it is necessary to take into account the ethical and legal aspects of the use of artificial intelligence in criminology, such as the transparency of algorithms, maintaining data confidentiality and preventing possible problems with decision-making. This work will examine how the use of artificial intelligence affects the effectiveness and reliability of crime detection and investigation, as well as what challenges and prospects this process is subject to in the context of forensic activities." Thus, scientific research in the proposed field should only be welcomed. Scientific novelty. The scientific novelty of the proposed article is beyond doubt. Firstly, it is expressed in the author's specific conclusions. Among them, for example, is the following conclusion: "artificial intelligence is able to provide significant assistance to law enforcement entities, but not replace them. The possibilities of using artificial intelligence systems for the purpose of solving and investigating crimes have not been determined at the moment. Neural network architectures effectively show the capabilities of artificial intelligence to analyze situations that arise during the disclosure and investigation of crimes and to put forward reasonable versions based on them on the circumstances of the commission of an illegal act. It is also necessary that special attention should be paid to high-quality training of the neural network on the use of modern forensic tools and methods in detection, disclosure and investigation, only after that it can be applied in real activities." These and other theoretical conclusions can be used in further scientific research. Secondly, the author offers suggestions on the practical use of modern technologies and the prospects for their implementation. For example, the author notes the following: "during the study, the possibility of using a neural network after its training was tested in order to compile a fragment of the descriptive part of the protocol of inspection of the scene based on orientation and survey photographs. AI coped with this task much better than most students studying the course of criminology. In order for artificial intelligence to effectively describe objects and documents, as well as signs of forgery and features of the subject, samples of the descriptive part of protocols from real criminal cases were initially uploaded to it. After that, the AI was able to compile a descriptive part of the document inspection protocol, where it described the document in detail, as well as the signs of forgery." The above conclusion may be relevant and useful for practical activities. Thus, the materials of the article may be of particular interest to the scientific community in terms of contributing to the development of science. Style, structure, content. The subject of the article corresponds to the specialization of the journal "Psychology and Psychotechnics", as it is devoted to legal problems related to the use of artificial intelligence technology in the detection and investigation of crimes. The content of the article fully corresponds to the title, since the author considered the stated problems and achieved the research goal. The quality of the presentation of the study and its results should be recognized as fully positive. The subject, objectives, methodology and main results of the study follow directly from the text of the article. The design of the work generally meets the requirements for this kind of work. No significant violations of these requirements were found. Bibliography. The quality of the literature used should be highly appreciated. The author actively uses the literature presented by authors from Russia and abroad (A.A. Bessonova, V.F. Vasyukova, R.I. Dremlyugi, I.A. Zavyalova, A.M. Kustova, A.I. Shemetova, D.V. Bakhteeva, Williams R.M., Ullman S. and others). Many of the cited scientists are recognized scientists in the field of criminology. Thus, the works of the above authors correspond to the research topic, have a sign of sufficiency, and contribute to the disclosure of various aspects of the topic. Appeal to opponents. The author conducted a serious analysis of the current state of the problem under study. All quotations of scientists are accompanied by author's comments. That is, the author shows different points of view on the problem and tries to argue for a more correct one in his opinion. Conclusions, the interest of the readership. The conclusions are fully logical, as they are obtained using a generally accepted methodology. The article may be of interest to the readership in terms of the systematic positions of the author in relation to the issues of improving the practice of using artificial intelligence technology in the detection and investigation of crimes. Based on the above, summing up all the positive and negative sides of the article, "I recommend publishing"