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ARTIFICIAL INTELLIGENCE AND CRIMINAL JUSTICE: PROSPECTS AND
PROBLEMS
NURPEISOVA ALMA KABIKESHOVNA
Candidate of Law Sciences, Associate Professor of the Department of General Legal and Special Disciplines, Karaganda university of Kazpotrebsoyuz Karaganda, Republic of Kazakhstan
SMAGULOVA AMINA SERIKOVNA
Graduate student of the Department of General Legal and Special Disciplines, Karaganda
university of Kazpotrebsoyuz Karaganda, Republic of Kazakhstan
Annotation: This article delves into the concept of artificial intelligence (AI) and examines its potential applications within the law enforcement and judicial frameworks. It identifies both the challenges and opportunities linked to the incorporation of AI technologies into these institutions. The discourse highlights the primary methods through which AI can enhance criminal investigations and streamline legal processes. Furthermore, it provides a comprehensive analysis of statistics pertaining to cybercrime, emphasizing trends in criminal activity conducted through digital means. The article also outlines the strategic initiatives of the state aimed at advancing the digital transformation of the law enforcement sector, reflecting a commitment to modernizing practices and improving efficacy in crime prevention and legal adjudication.
Keywords: artificial intelligence, electronic criminal case, judicial system, law enforcement system, decision-making, investigative and operational activities.
In the context of contemporary society, which is increasingly oriented towards the comprehensive digitalization of governmental frameworks, the evolution of law enforcement and judicial systems into digital environments is an inevitable development. This transformation is driven by the necessity for enhanced efficiency, transparency, and responsiveness in public administration. As digital technologies advance, they offer significant opportunities for the modernization of traditional practices, enabling a more streamlined approach to law enforcement and judicial processes. The integration of digital tools is anticipated to facilitate improved data management, enhance communication channels, and optimize the overall functionality of these institutions. Therefore, the shift towards a digital paradigm within law enforcement and judicial systems is not merely a possibility but rather an essential progression in aligning with global trends in governance and public service delivery.
According to the Strategic Development Plan of Kazakhstan until 2025, approved by the Decree of the President of the Republic of Kazakhstan on February 15, 2018, №636, the digitalization of internal procedures, office work, the process of handling appeals using elements of artificial intelligence and other advanced analytical tools is one of the priorities for law enforcement agencies.
One of the key aspects of this transition is outlined in the Order of the Prosecutor General of the Republic of Kazakhstan dated January 3, 2018, №2, in the Instruction on Conducting Criminal Proceedings in Electronic Format, which introduced the «Electronic Criminal Case» module in the previously implemented database, the «Unified Register of Pre-trial Investigations».
An analysis of data from the Legal Statistics Internet Portal of the Committee on Legal Statistics and Special Accounts of the Prosecutor General's Office of the Republic of Kazakhstan shows that in 2024, 35,320 criminal offenses were registered in Kazakhstan, of which 33,537 investigations were initiated in electronic format. Similarly, in 2023, 140,272 criminal offenses were registered, of which 131,770 were initiated electronically [2].
These statistics suggest that the digitalization of criminal proceedings transcends mere relevance; it has become an established reality within contemporary legal frameworks. Consequently, it is imperative to address the integration of artificial intelligence (AI) into the operations of law enforcement agencies. The incorporation of AI technologies holds the potential to significantly enhance various aspects of criminal justice, including evidence analysis, case management. By leveraging AI, law enforcement can improve the efficiency and accuracy of investigations, thereby facilitating a more proactive approach to crime prevention. As such, the discourse surrounding the implementation of AI in this sector must be prioritized, considering both the transformative benefits and the ethical implications associated with its use. This examination will provide a foundational understanding necessary for the effective incorporation of AI in law enforcement practices.
The fundamental objective is to elucidate the definition of artificial intelligence (AI). The term «artificial intelligence» was introduced by John McCarthy, a prominent figure in the field and a member of the United States National Academy of Sciences, during the Dartmouth Conference in 1956. McCarthy articulated that AI encompasses the capability of machines—specifically, robots, software programs, and complex systems—to engage in cognitive and creative tasks traditionally associated with human intelligence. This includes the autonomous ability to identify and solve problems, derive conclusions from available data, and make informed decisions.
Over the decades, the scope of AI has expanded significantly, incorporating various subfields such as machine learning, natural language processing, and computer vision. These advancements have enabled AI systems to not only mimic human-like reasoning but also to process vast amounts of information with speed and accuracy that often surpass human capabilities. Therefore, understanding the foundational definition of AI is crucial for exploring its applications and implications within various domains, particularly in law enforcement and judicial systems.
Simultaneously, Howard Gardner, a distinguished scholar from Harvard University, has delineated four distinct types of intelligence that contribute to our understanding of artificial intelligence:
Acting like a human being: This form of intelligence pertains to a computer's ability to emulate human behavior effectively. A key measure of this capability is the Turing Test, proposed by Alan Turing, which evaluates whether a machine can exhibit behavior indistinguishable from that of a human. When a computer successfully mimics human actions and responses in various scenarios, it demonstrates a proficiency in this domain.
Thinking like a human being: This type of intelligence involves a computer's capacity to engage in cognitive processes akin to those of humans. It encompasses problem-solving abilities that require human-like reasoning, as opposed to merely executing mechanical tasks. An example of this is the development of autonomous vehicles, which necessitate complex decision-making similar to that performed by human drivers in real-time environments.
Thinking rationally: This category focuses on the analytical study of human thought processes through the establishment of normative models. By understanding how humans typically think and behave in various contexts, researchers can formulate rules and frameworks that characterize standard cognitive behaviors. These models aid in designing AI systems that align with rational decisionmaking paradigms.
Acting rationally: This intelligence type emphasizes the practical application of behavioral studies. By examining how individuals respond to specific situations and conditions, it becomes possible to identify effective strategies and actions. This understanding can inform the development of AI systems that not only simulate human-like reasoning but also apply rational methods to achieve optimal outcomes in diverse scenarios.
Gardner's typology enriches the discourse surrounding artificial intelligence by providing a structured framework for analyzing its multifaceted nature and its implications across various fields, including law enforcement and judicial systems [3, pp. 31-33].
Given the diverse capabilities of artificial intelligence, it is pertinent to consider its integration into the process of handling citizen appeals. The incorporation of AI into this procedure has the
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potential to significantly mitigate corruption risks that may arise from the actions of law enforcement personnel.
By automating the intake and processing of appeals, AI systems can ensure a consistent and impartial approach to evaluating citizen submissions. This technological intervention can reduce human discretion, which is often a vector for corrupt practices, thus enhancing transparency in the handling of complaints and requests. Moreover, AI can facilitate real-time monitoring and analysis of appeal data, enabling the identification of patterns that may indicate misconduct or systemic issues within law enforcement agencies.
Furthermore, the use of AI in processing citizen appeals can improve the efficiency and speed of response, ensuring that concerns are addressed promptly. By implementing a standardized protocol for appeal management, AI can contribute to a more reliable and accountable system that fosters public trust in law enforcement institutions. Thus, the strategic adoption of artificial intelligence in this context not only aims to streamline operations but also reinforces ethical standards and integrity within the law enforcement framework.
At the same time, according to the Law on Informatization, blockchain is an information and communication technology that ensures the immutability of information in a distributed data platform based on a chain of interconnected data blocks, using specified integrity confirmation algorithms and encryption tools [4].
In this regard, one of the possibilities of using artificial intelligence systems is the transfer of court archives, as well as systems of the Committee on Legal Statistics and Special Accounts of the Prosecutor General's Office of the Republic of Kazakhstan, to blockchain. This would exclude the possibility of data loss due to hacking or corruption motives.
Artificial intelligence serves a significant function in the realm of criminal investigations, acting as a mechanism for the systematic recording and analysis of data gathered during investigative and operational activities. Its capabilities enable law enforcement agencies to efficiently process vast amounts of information, facilitating the identification of trends, correlations, and anomalies that may be critical to solving cases.
The aforementioned points highlight that AI primarily functions as a tool for data collection, analysis, and storage within criminal proceedings. This role is crucial not only for enhancing the efficacy of investigative efforts but also for mitigating corruption risks among law enforcement officials. By employing AI systems, agencies can achieve greater transparency and accountability in their operations, as these technologies can track and audit interactions and decisions made during investigations.
Moreover, we have also explored the potential for artificial intelligence to be utilized in addressing issues within the judicial system, particularly regarding the possibility of replacing human personnel in certain roles. While the introduction of AI in judicial and law enforcement contexts could lead to increased efficiency and objectivity, it raises significant ethical and practical considerations. The potential for AI to assume roles traditionally held by human judges or law enforcement officers necessitates careful deliberation concerning the implications for justice, discretion, and the interpretation of the law.
Thus, while AI can significantly enhance the functionality and integrity of criminal justice processes, its implementation as a substitute for human personnel must be approached with caution, ensuring that the fundamental principles of justice and human oversight are upheld.
Thus, H.D. Alikperov, the developer of the computer program «Electronic Scales of Justice», justified the use of artificial intelligence in determining the limits of individual sanctions against a particular defendant [5, p. 38].
At the same time, it should be borne in mind that artificial intelligence formally meets the requirements of a fair trial, including the procedural aspects that ensure a person's participation at all stages of legal proceedings, with adherence to the principles of adversariality and equality of the parties, as well as reasonable time for case consideration [5, p. 85].
However, a significant challenge associated with this approach is the inherent absence of moral and emotional self-awareness within artificial intelligence systems. The decision-making processes of AI are predominantly grounded in the logical frameworks and algorithms programmed into them, which can lead to conclusions that overlook the complexities of human psychology and emotional states.
In criminal investigations, this limitation can be particularly problematic. For instance, when assessing suspects, AI may generate decisions based solely on data patterns and logical inference, failing to account for the nuanced psychological factors that could influence behavior or response. This oversight could result in misinterpretations of intent or culpability, potentially compromising the integrity of legal outcomes.
Similarly, during the interrogation of victims, the reliance on AI could skew the process. Victims may withhold critical information for a multitude of reasons, including trauma, fear, or a desire to protect others. An AI system, lacking the capacity for empathetic understanding, may not be equipped to recognize these subtleties, potentially leading to incomplete or inaccurate assessments of the situation. The inability of AI to engage with the emotional dimensions of human experiences raises ethical concerns about its efficacy and reliability in high-stakes environments such as law enforcement and judicial proceedings.
Therefore, while AI can enhance efficiency and provide analytical capabilities, its limitations in understanding human emotions and moral nuances must be critically evaluated. The integration of AI in these sensitive areas necessitates a balanced approach that incorporates human oversight and emotional intelligence to ensure that justice is administered fairly and comprehensively.According to the well-founded statement by the famous American lawyer J. Marshall, «judicial power is never exercised to implement the will of the judge, but always to implement the will of the legislator...» [6, pp. 50-51].
Similarly, the entire system of law enforcement agencies is aimed at implementing the law, rather than pursuing personal beliefs. In this regard, the use of artificial intelligence could significantly affect the current model of humanizing the judiciary and efforts to rehabilitate convicted individuals. The work carried out by law enforcement agencies is aimed not only at investigating criminal cases in accordance with the law, but also at conducting subtle psychological work with suspects, victims, and sometimes even witnesses.
Such thought processes, based on emotional development, are beyond the control of artificial intelligence. Therefore, in our opinion, its introduction into the work of law enforcement and judicial systems is primarily technical in nature. For example, American judges often use programs to assess the likelihood of a suspect reoffending [7].
This practice is applicable for analyzing possible court decisions, preventing repeat offenses, and digitalizing criminal proceedings to mitigate corruption risks or the issue of lost criminal cases.
The introduction of artificial intelligence as an analytical assistant in conducting investigative and operational activities is an urgent matter, which, if implemented, could significantly simplify the work of law enforcement agencies.
In light of the preceding analysis, we assert that artificial intelligence represents a significant enhancement to the operations of law enforcement and judicial systems. The integration of AI technologies can facilitate a more rapid and efficient execution of investigative and operational measures, thereby augmenting the technical capabilities of these institutions. AI can assist structural units in analyzing vast datasets, which may lead to a reduction in errors during data review and decision-making processes.
However, it is our conviction that the complete replacement of human personnel by artificial intelligence is not feasible due to several critical factors previously discussed. The limitations of AI in understanding human emotions, moral considerations, and the complexities of individual circumstances underscore the necessity of human involvement in these sensitive areas. The nuances of human interaction, empathy, and ethical judgment cannot be fully replicated by AI systems, making it essential for human oversight to remain integral in law enforcement and judicial processes.
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Therefore, it is more prudent to conceptualize artificial intelligence as a valuable technical assistant within the broader framework of digitalizing criminal proceedings. By positioning AI as a supportive tool rather than a substitute, law enforcement agencies can leverage its strengths while retaining the essential human elements required for effective and just administration of the law. This approach not only enhances operational efficiency but also ensures that the fundamental principles of justice and accountability are upheld in the digital age.
LIST OF SOURCES USED:
1. On approval of the Instruction on conducting criminal proceedings in electronic format: Order of the Prosecutor General of the Republic of Kazakhstan dated January 3, 2018 No. 2 // The reference control Bank of the NPA of the Republic of Kazakhstan in electronic form. 2018.
2. Internet portal of legal statistics of the Committee on Legal Statistics and Special Accounts of the Prosecutor General's Office of the Republic of Kazakhstan // https://qamqor.gov.kz/
3. Müller, John Paul, Massaron, Luca. Artificial Intelligence For Dummies: Translated from English. — St. Petersburg: Dialectica LLC, 2019. — 384 p.: ill. — Parallel title in English.
4. Shaihmetov, Sh. Sh., Eshnazarov, A. A. The Application of Blockchain Technologies in the Activities of Law Enforcement Agencies // International Scientific Journal "Science and Life of Kazakhstan". - 2019. - No. 7/1. - Pp. 94-98.
5. Alikperov, K. D. New Approaches to Understanding the Essence of Punishment. Problem Statement / K. D. Alikperov. — EDN OPWYTJ // Criminal Procedure. — 2020. — No. 2. — Pp. 36-42.
6. Entin, M. Fair Trial under the Law of the Council of Europe and the European Union / M. Entin.
— EDN HSLMKB // Constitutional Law: Eastern European Review. — 2003. — No. 3. — Pp. 85-97.
7. Kuzmina, A. V. Legal Interest in the Development of Personal Legal Consciousness / A. V. Kuzmina. — EDN TEGDST // Bulletin of Moscow State University of Culture and Arts. — 2014.
— No. 6 (62). — Pp. 50-51.
8. Maslow, A. Motivation and Personality / A. Maslow. — St. Petersburg: Piter, 2008. — 352 p.
