The Role of Artificial Intelligence in the Eradication of Transnational Crime

The Role of Artificial Intelligence in the Eradication of Transnational Crime

Folajimi O. Jejelola

University of Ibadan

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8110069

Received: 04 November 2024; Accepted: 12 November 2024; Published: 03 December 2024

ABSTRACT

Transnational crime, ranging from human trafficking and drug smuggling to cybercrime and terrorism, presents an increasingly complex challenge to global security. The interconnectedness of the modern world, coupled with advancements in communication and transportation technologies, has enabled criminal networks to operate with greater efficiency and secrecy, exploiting legal loopholes and jurisdictional limitations. Traditional law enforcement methods have struggled to keep pace with these evolving threats. Artificial Intelligence (AI) has emerged as a transformative tool in the fight against transnational crime, offering real-time data analysis, pattern recognition, and predictive capabilities that enhance law enforcement efforts. This study critically assesses the role of AI technologies, such as machine learning, predictive analytics, and biometric systems, in detecting, preventing, and eradicating transnational crimes, including cybercrime, human trafficking, and terrorism. To assess AI’s effectiveness, this study uses a mix of doctrinal and quantitative research technique, referencing case studies and comparative analysis from various institutional and geographic contexts. This methodology facilitated the organized gathering, analysis, and interpretation of data, concentrating on the tangible effects of AI systems in law enforcement. The study emphasizes AI’s capacity to transform crime prevention and investigation, addressing ethical, legal, and operational obstacles like data privacy issues, algorithmic bias, and the need for international collaboration. The report advocates for uniform legal frameworks and international collaboration, offering proposals that reconcile technological progress with the safeguarding of human rights, with the objective of facilitating responsible AI inclusion in global security efforts.

Keywords: Artificial Intelligence, Law Enforcement, Transnational Crime Detection, Predictive Policing, Machine Learning

INTRODUCTION

Transnational crime represents an ever-growing threat to global security, as criminal organizations increasingly exploit the interconnectedness of the modern world to carry out illicit activities that span borders.[1] Human trafficking, drug smuggling, cybercrime, terrorism, and weapons trafficking damage national stability, disrupt economies, and significantly degrade human rights. The intricacy and extent of international crime are exacerbated by technological, communicative, and transportation improvements, enabling criminal networks to function more clandestinely and effectively than before. These networks use regulatory gaps, variations in legal systems, and jurisdictional constraints to elude apprehension, rendering it exceptionally challenging for law enforcement organizations to maintain effectiveness. The international community is under increasing pressure to devise more advanced and imaginative ways to successfully address these global problems.[2] Artificial intelligence (AI) has emerged as a powerful tool in the fight against transnational crime, offering a range of advanced technologies that can significantly enhance law enforcement efforts. Unlike traditional methods, which often rely on manual processes and are limited by the sheer volume of data and the geographic spread of criminal networks, AI has the ability to analyze massive amounts of information in real-time, identifying patterns and detecting anomalies that would likely go unnoticed by human investigators. AI technologies such as machine learning, deep learning, predictive analytics, and natural language processing (NLP) have the potential to revolutionize crime prevention and investigation by providing law enforcement with faster, more accurate insights into criminal behavior and emerging threats. For example, AI-driven systems can analyze vast datasets of financial transactions to detect money laundering schemes, flagging suspicious activities across borders with speed and precision that far surpasses human capabilities.[3] One of the most significant advantages of AI is its ability to predict and prevent crime before it happens. By utilizing machine learning algorithms that continuously analyze historical crime data, AI can help law enforcement agencies identify trends and forecast where and when future crimes are likely to occur. This predictive capability allows authorities to allocate resources more efficiently and intervene proactively, disrupting criminal activities before they escalate. AI has also been instrumental in enhancing surveillance and monitoring systems, particularly through the use of facial recognition and biometric technologies. These tools have proven invaluable in identifying and tracking criminals as they move across borders, often under assumed identities, and have been used to apprehend individuals involved in human trafficking, terrorism, and organized crime. Moreover, NLP algorithms enable the monitoring and analysis of criminal communications across multiple languages and platforms, aiding in the identification of cross-border criminal networks.[4] Despite these advancements, the integration of AI into global crime-fighting efforts presents a host of challenges. The use of AI in law enforcement raises important ethical concerns, particularly in relation to privacy and civil liberties. AI systems often require access to vast amounts of personal data, and the widespread use of surveillance technologies can lead to potential overreach, as governments and law enforcement agencies may infringe on individual privacy rights in the name of security. Moreover, the accuracy and fairness of AI algorithms are critical issues that must be addressed. Studies have shown that AI systems can reflect biases present in the data they are trained on, leading to discrimination and unequal treatment, particularly in relation to race, ethnicity, and socio-economic status.[5] These biases can result in false positives or over-policing of certain populations, undermining public trust in law enforcement and exacerbating social inequalities. The challenge of ensuring that AI is deployed ethically and fairly is further complicated by the transnational nature of the crimes it is intended to combat. Criminal networks operate across multiple jurisdictions, often taking advantage of the fact that legal frameworks and law enforcement capabilities vary significantly from one country to another.[6] In many cases, international cooperation is necessary to track and apprehend criminals, yet the lack of harmonized regulations surrounding the use of AI and data sharing between nations creates significant obstacles. For AI to be truly effective in eradicating transnational crime, countries must work together to develop standardized protocols, share critical intelligence, and establish oversight mechanisms that ensure AI is used responsibly and within the bounds of the law. The efficacy of AI in this context depends not only on technical progress but also on the capacity of the international community to establish robust alliances and promote cross-border cooperation. Alongside these ethical and legal issues, substantial technical constraints must also be acknowledged. Although AI has advanced considerably in data analysis and illicit activity detection, it is not flawless. Artificial intelligence systems are only as successful as the data on which they are trained, and often, the data accessible to law enforcement is inadequate, obsolete, or erroneous.[7] Furthermore, criminal networks are continuously evolving, developing new methods to evade detection and circumvent AI-driven systems. This ongoing arms race between law enforcement and criminals requires continuous innovation and refinement of AI technologies to ensure they remain effective in combating emerging threats. The ability of AI to adjust to the fluid and dynamic characteristics of international crime will be a crucial factor in its long-term efficacy. This research seeks to thoroughly investigate the role of artificial intelligence in combating transnational crime, analyzing the accompanying prospects and obstacles of its application. This project will evaluate the effectiveness of AI in improving worldwide initiatives to deconstruct transnational criminal networks by examining its existing uses in crime prevention, detection, and investigation. The project will critically assess the ethical, legislative, and technical problems associated with the use of AI in law enforcement, providing ideas on how to resolve these concerns to enable successful and responsible deployment of AI. This research aims to enhance the discourse on the future of AI in global security by analyzing case studies and emerging technologies, and offering recommendations for policymakers, law enforcement agencies, and global organizations on effectively utilizing AI to combat transnational crime.[8]

Aim and Objectives

This study aims to explore the transformative potential of artificial intelligence (AI) in combating transnational crime by examining both its practical applications and associated challenges, as well as its consequences for law enforcement, ethics, and international politics.

The primary objective is to critically assess how AI technologies, such as machine learning, predictive analytics, and biometric systems, are being used to detect, prevent, and eradicate major forms of transnational crime, including human trafficking, cybercrime, terrorism, and drug smuggling. This research will evaluate the effectiveness of these technologies in enhancing law enforcement capabilities, particularly in the context of crimes that span multiple jurisdictions and evade traditional crime-fighting methods. In addition to exploring the practical benefits of AI, the study also seeks to examine the ethical and operational challenges that arise from its deployment in global crime-fighting efforts. Specifically, the research will focus on concerns related to data privacy, algorithmic bias, and the potential misuse of AI-driven surveillance systems. Furthermore, this study will evaluate the need for international cooperation and regulatory alignment to ensure that AI technologies are used responsibly and ethically across borders. The objective is to provide a balanced view of both the opportunities and limitations of AI in eradicating transnational crime, offering recommendations for improving its application in law enforcement.

Research Questions

To guide the investigation, this study will address the following research questions:

1. How does artificial intelligence contribute to the detection, prevention, and eradication of major forms of transnational crime such as human trafficking, cybercrime, and terrorism?
2. What are the key ethical and practical challenges involved in the deployment of AI technologies in combating transnational crime, and how can these challenges be addressed through international cooperation and regulation?

METHODOLOGY

This research employs a methodology that is predominantly doctrinal, with a mix of quantitative analysis to evaluate the impact of artificial intelligence (AI) on transnational crime prevention. This method entailed the methodical gathering, analysis, and interpretation of data from diverse sources, concentrating on comprehending the applicability, efficacy, and obstacles of AI-driven technologies in international law enforcement settings. The research utilizes various case studies in which AI tools, including predictive analytics, biometric identification, and machine learning, have been employed, examining their direct effects on crime detection, prevention, and elimination. A comparative analysis was undertaken across several geographic and institutional frameworks to evaluate the practical consequences and outcomes of AI deployment, emphasizing both successful implementations and limits. This methodological framework facilitates a thorough understanding of AI’s role in crime reduction by analyzing the intersections of technology with law enforcement techniques, international collaboration, and policy development. The ethical, legal, and operational difficulties related to AI utilization in law enforcement were thoroughly analyzed. This involved a comprehensive examination of the ethical ramifications of AI in surveillance and data privacy, evaluating how these issues influence public trust and the legal limitations on the implementation of AI technology. The paper includes a critical evaluation of existing literature to situate the function of AI in transnational crime prevention within broader theoretical frameworks. This stratified method seeks to offer a comprehensive perspective on AI’s strengths and weaknesses in tackling intricate, global criminal enterprises. This methodology provides a detailed study that highlights AI’s revolutionary potential and promotes the establishment of standardized international norms to address ethical and operational difficulties, assuring the appropriate use of AI in global security initiatives.

LITERATURE REVIEW

The literature surrounding the role of artificial intelligence (AI) in combating transnational crime is vast and growing, reflecting the increasing awareness of AI’s potential to transform global security efforts.[9] Transnational crime encompasses a wide range of illicit activities, including human trafficking, cybercrime, drug smuggling, terrorism, and arms trafficking, which operate across borders and evade traditional law enforcement strategies. Scholars have long emphasized the need for more sophisticated and innovative approaches to tackle these crimes, given their complex, borderless nature and the rapid technological advancements that criminal networks exploit.[10] As globalization has facilitated the movement of goods, people, and information, it has also provided fertile ground for organized crime to operate, leaving governments and international agencies struggling to keep pace. AI has thus emerged as a promising solution to this challenge, offering advanced tools that can enhance the detection, prevention, and eradication of transnational criminal activities.[11]

The research by Akanksha Mishra provided an in-depth exploration of how artificial intelligence (AI) is fundamentally transforming the landscape of crime detection and prevention.[12] The primary aim of the study is to evaluate how AI technologies can enhance law enforcement efforts by improving predictive capabilities, increasing efficiency in forensic analysis, and aiding criminal profiling. The objectives include analyzing specific areas where AI is most effective, such as predictive policing, real-time video surveillance, and the analysis of forensic evidence, and how these innovations can reduce crime rates and increase the speed of criminal investigations. Methodologically, the research adopts a combination of literature review and case studies from various law enforcement agencies globally that have implemented AI solutions.[13] For instance, the study highlights a predictive policing program used by the Los Angeles Police Department, which applied AI algorithms to analyze historical crime data and identify patterns. This approach allowed law enforcement agencies to allocate resources more efficiently, reducing crime rates in specific high-risk areas. The use of AI in video surveillance is another key focus of the study, demonstrating how AI-powered cameras can monitor public spaces in real time, automatically detecting suspicious behavior and alerting authorities. The findings of the research are significant. AI technologies, when applied in crime prevention, enhance the ability of law enforcement to predict, detect, and deter criminal activities before they occur. For example, predictive policing systems can reduce the incidence of crime by 30 to 40 percent, and ensure the speedy delivery of emergency service by 30 to 35 percent.[14] Moreover, AI’s application in forensic science has expedited the analysis of evidence, solving cold cases that would have taken years or even decades to resolve using traditional methods. In criminal profiling, AI has been instrumental in decoding behavioral patterns, thereby helping to predict criminal motives. The study also raises important ethical considerations, such as the potential invasion of privacy, data security concerns, and the risk of algorithmic biases leading to discrimination. Mishra and her colleagues recommend that as AI becomes increasingly integrated into law enforcement, it is essential to establish regulatory frameworks to ensure transparency, accountability, and fairness in the deployment of these technologies. The authors advocate for responsible AI use that balances crime prevention with the protection of civil liberties.

Pratima Gund et al’s paper focused on the role of AI in augmenting the capabilities of the criminal justice system.[15] The study’s main aim is to evaluate how AI can predict criminal behavior, detect crimes in real-time, and prevent future offenses, particularly in the context of rising cybercrime. The objectives include examining AI’s role in automating decision-making processes in law enforcement, investigating how AI enhances accuracy in crime detection, and evaluating its effectiveness in reducing crime rates globally. The methodology employed by Patil et al. consists of analyzing crime data from law enforcement agencies that have adopted AI technology. For instance, they discuss how AI-driven crime prediction models have enabled law enforcement agencies to reduce crime rates in the United States, with significant improvements seen in urban centers where AI technologies were implemented. The study also focuses on AI’s ability to automate the analysis of massive datasets, allowing real-time identification of suspicious activities.[16] The findings of the research indicate that AI has led to a 3.3% reduction in crime rates in certain regions of the United States and a 6.3% drop in violent crimes in areas where AI-powered surveillance systems were in use. The authors highlight AI’s ability to predict criminal behavior through data analytics, giving law enforcement agencies the tools to deploy officers more strategically. In addition to crime detection, AI has been instrumental in solving cybercrimes, as it can quickly analyze and track cyber threats, including ransomware attacks. The study’s conclusion emphasizes the importance of AI in the criminal justice system but also warns against the potential dangers of over-reliance on technology. The researchers raise ethical questions about the accuracy of AI judgments in criminal cases, particularly when it comes to decisions related to sentencing and parole. They recommend that AI should be used to assist human decision-making, not replace it, and call for a legal framework that governs AI use in criminal justice to ensure fairness and prevent wrongful convictions.

In another study, the researchers investigated a novel and increasingly relevant question: Can artificial intelligence (AI) be considered a subject of crime?[17] The study aims to address this question by analyzing AI’s potential culpability in legal terms, focusing on whether AI can possess the necessary elements of criminal guilt (Mens Rea and Actus Reus). The objectives are twofold: (1) to examine international case studies and legal frameworks that involve crimes committed using AI, and (2) to investigate whether AI could one day be held criminally responsible for its actions, either directly or through its creators. This research employs a wide range of international legal sources and comparative analysis. The researchers analyze national legislations, international conventions, and scientific literature to determine the feasibility of recognizing AI as a criminal subject. Using comparative law, they evaluate the readiness of legal systems across the globe to handle crimes committed by or with the assistance of AI technologies. The findings of the study are intriguing. While AI is not yet capable of autonomous action to the degree necessary for criminal responsibility, the rapid pace of AI development suggests that this may soon change. The research identifies that AI already possesses the ability to perform tasks that, under certain circumstances, could be harmful. For instance, AI systems involved in financial markets or automated vehicle control could cause substantial harm if they malfunction or are used with malicious intent. As such, the researchers propose that legal systems start preparing for the possibility of assigning liability to AI developers, users, or even the AI systems themselves in the future.

International Laws on Artificial Intelligence

In recent years, the convergence of AI and international law has gained significance, particularly as nations and organisations endeavour to leverage AI’s potential to tackle intricate challenges such as transnational crime.[18] The swift advancement of AI presents significant technological, ethical, and legal challenges. This section succinctly analyses six international laws, treaties, and frameworks that presently govern the global utilisation of AI, seeking to reconcile scientific progress with ethical and security considerations. These frameworks influence the application of AI across various domains, such as privacy protection, cybersecurity, human rights, and international security, hence indirectly addressing AI’s potential role in combating transnational crime.

The OECD Principles on Artificial Intelligence (2019)

In 2019, the Organisation for Economic Co-operation and Development (OECD) established the OECD Principles on Artificial Intelligence, one of the initial globally acknowledged frameworks to advocate for the ethical use of AI.[19] These principles, updated in 2024, underscore equity, clarity, and responsibility, urging nations to implement AI technologies in manners that uphold human rights and democratic ideals. The principles, although non-binding, have received endorsement from over 40 nations, including prominent AI developers such as the United States, Japan, and the European Union, indicating a robust consensus on the responsible utilisation of AI. The OECD principles promote a human-centered approach to AI, asking governments to formulate policies that prioritise the welfare of individuals and society as a whole. In the realm of transnational crime, these principles advocate for law enforcement agencies to utilise AI ethically, honouring privacy rights while implementing AI-driven tools for functions such as crime prediction, surveillance, and international collaboration in criminal investigations. Over 50 countries seem to have national and regional legal framework for the incorporation of AI in their domain[20] with over “930 policy initiatives across 71 jurisdictions in the OECD.AI national policy database​.”[21]

The European Union’s General Data Protection Regulation (GDPR) (2018)

The General Data Protection Regulation (GDPR) of the European Union, enacted in 2018, is one of the most extensive legal frameworks regulating data privacy and protection.[22] While not exclusively an AI regulation, GDPR encompasses essential elements of AI, especially in relation to data utilisation, processing, and personal rights pertaining to their data. The GDPR mandates transparency and accountability in data processing, essential for the application of AI in law enforcement. AI systems employed to forecast or detect future criminal conduct must adhere to the stringent regulations of GDPR regarding data utilisation, which include restrictions on data retention, guarantees of data security, and provisions for individuals to access or erase their personal information. The “right to explanation” provided in Articles 13-15 and Article 22 of the GDPR is especially pertinent to AI, as it mandates transparency in automated decision-making processes. This mandate ensures that AI applications in transnational crime prevention are utilised responsibly and subjected to inspection, hence reducing the dangers of prejudice or inaccuracy in AI systems.

The United Nations Guiding Principles on Business and Human Rights (UNGP) (2011)

The United Nations Guiding Principles on corporate and Human Rights (UNGP) establish an international benchmark for mitigating and addressing the negative effects of corporate operations, especially those associated with AI, on human rights.[23] Established in 2011, these principles pertain to both corporations and governments, emphasising the obligation to safeguard, honour, and rectify human rights in their activities. As artificial intelligence becomes more prevalent in private sector activities, especially those fulfilling governmental roles such as security and surveillance, the UNGP advocates for firms to perform due diligence to avert human rights violations. Government agencies frequently utilise AI techniques created by private firms to address transnational crime. By complying with UNGP guidelines, corporations and governments guarantee that AI applications, particularly in criminal justice and border control, do not violate basic rights. This paradigm promotes openness, non-discrimination, and accountability, facilitating international collaboration in the responsible deployment of AI to combat global crime while upholding ethical standards.

 The Council of Europe’s Convention on Cybercrime (Budapest Convention) (2001)

The Budapest Convention, instituted by the Council of Europe in 2001, is the inaugural international convention focused on combating cybercrime. The treaty, despite predating the current AI age, holds considerable significance for AI applications in preventing cybercrime and eradicating transnational crime. It establishes directives for member states for the collection of electronic evidence, cross-border collaboration, and cybercrime legislation, thereby fostering a unified worldwide strategy for cyber-related matters. The agreement establishes a legal framework for international cooperation among law enforcement agencies in the investigation of cybercrimes, including human trafficking, financial crimes, and drug trafficking, which can be enabled through cyber networks, as AI becomes more prevalent in cybersecurity. The Budapest Convention’s focus on collaboration is essential, as AI-driven systems for identifying, tracing, and prosecuting cybercriminals greatly benefit from coordinated international efforts. The agreement promotes ethical concerns in data management, especially in light of AI’s capabilities in data analysis, ensuring that international operations uphold privacy rights and adhere to established legal requirements.

The United Nations International Telecommunication Union (ITU) AI for Good Global Summit (2017)

The ITU’s AI for Good Global Summit, initiated in 2017, is a worldwide effort to leverage AI for sustainable development while mitigating related dangers.[24] This UN-led project, while not legally enforceable, delineates guiding principles for the ethical application of AI to promote social good, encompassing issues of security and justice. The summit convenes governments, technology firms, and civil society to establish norms and standards for the deployment of AI across several sectors, including law enforcement. AI for Good offers a venue for discourse on the use of AI technology in the eradication of transnational crime, supporting global security goals such as the detection of illicit financial flows, surveillance of people trafficking networks, and identification of organised crime trends. The summit emphasises the necessity for transparency, accountability, and global collaboration, highlighting that although AI can serve as an effective instrument in crime prevention, it must be utilised judiciously to avert misuse and uphold human rights (Arif et al., 2016; Dreyer et al., 2018).

The UNESCO Recommendation on the Ethics of Artificial Intelligence (2021)

Adopted in 2021, UNESCO’s Recommendation on the Ethics of Artificial Intelligence becomes one of the inaugural worldwide guidelines specifically focused on AI ethics.[25] This paper, while not legally enforceable, is significantly influential and delineates concepts and values that governments and organisations ought to contemplate while formulating and implementing AI technologies. UNESCO’s guidelines underscore the importance of human rights, environmental sustainability, and the advancement of peace, urging nations to guarantee that AI technology are employed in manners that do not intensify inequality or violate privacy. This paradigm advocates for the transparent and accountable application of AI in combating international crime, ensuring that AI-driven monitoring or predictive policing does not disproportionately affect particular populations or infringe upon rights to privacy and due process. It also promotes worldwide data-sharing protocols to prevent and combat global crime, acknowledging that ethical AI practices are essential for sustaining public trust in AI technologies employed in law enforcement. The UNESCO guideline underscores the necessity of regular evaluations and global supervision to guarantee that AI technologies develop in accordance with ethical standards, especially in critical domains such as crime prevention and national security.

Applications of AI in Combating Transnational Crime

Artificial Intelligence has already demonstrated its profound potential in combating various forms of transnational crime, with several notable applications across different sectors of law enforcement.[26] One prominent area where AI has made significant strides is in the detection and prevention of human trafficking. Criminal networks often exploit the anonymity of the internet and dark web to traffic people across borders, making it challenging for traditional law enforcement methods to monitor these activities. AI-driven tools, however, have been employed to scan vast amounts of online data in real-time, identifying suspicious activities, communications, and patterns associated with trafficking. By analyzing social media activity, language patterns, and hidden signals within networks, AI systems have been instrumental in identifying trafficking routes and monitoring the movements of traffickers.[27] These technologies have enabled law enforcement to disrupt trafficking operations more effectively and, in several instances, have directly led to the rescue of trafficking victims.[28] AI’s ability to process large datasets quickly has allowed law enforcement to track and apprehend traffickers who would otherwise remain undetected.

AI has also shown great promise in the realm of cybercrime prevention, particularly in addressing the growing threat of financial fraud, hacking, and identity theft, all of which are often transnational in nature. Cybercriminals exploit digital platforms to carry out cross-border crimes that are difficult to trace using conventional methods. AI’s ability to analyze patterns in cyber activities has been critical in detecting anomalies that signal potential cyberattacks, such as phishing schemes and ransomware assaults. AI-powered cybersecurity systems have proven to be more adept at real-time detection and neutralization of cyber threats than traditional systems, enabling quicker responses to emerging threats.[29] Financial institutions and multinational corporations have adopted AI to secure their operations, flagging suspicious transactions and preventing fraud on an international scale. These innovations have not only protected businesses and consumers but have also helped to trace and dismantle organized cybercrime networks.

In addition, AI has also been used to combat drug smuggling and organized crime. For instance, AI systems that monitor global shipping data, financial transactions, and communication channels have been utilized to detect unusual patterns indicative of smuggling operations. AI’s ability to analyze the movement of goods across borders and correlate this with known smuggling activities has led to significant breakthroughs in intercepting illegal drug shipments.[30] In some cases, AI technology has been used to predict smuggling routes, allowing law enforcement agencies to preemptively intervene. These applications have not only reduced the prevalence of drug smuggling but have also disrupted entire criminal organizations involved in international drug trafficking.

Moreover, AI has played a critical role in counterterrorism efforts. The use of AI in surveillance and monitoring systems has allowed governments and security agencies to track suspected terrorists more efficiently and accurately. By integrating facial recognition technologies and biometric systems into their operations, law enforcement agencies can identify individuals attempting to move across borders under false identities or using forged documents. AI’s capacity to monitor communications across multiple languages and platforms has also been crucial in uncovering terrorist networks operating transnationally. In several instances, AI technologies have helped prevent terrorist plots by identifying threats before they materialized, highlighting the potential of AI to not only detect crime but also to anticipate and prevent future attacks.[31]

These real-world applications of AI in combating transnational crime demonstrate the technology’s transformative impact on global security. By enhancing law enforcement’s ability to process vast amounts of data, predict criminal behavior, and intervene proactively, AI has contributed to more effective policing on an international scale. However, as these systems evolve, continuous innovation and collaboration will be needed to address the growing sophistication of criminal networks, ensuring that AI remains an indispensable tool in the fight against transnational crime.

Ethical and Legal Challenges in the Use of AI for Law Enforcement

The use of Artificial Intelligence in law enforcement, while offering promising advancements in the fight against transnational crime, raises significant ethical and legal challenges that demand careful consideration.[32] A primary worry is the matter of privacy. AI systems, especially those used for surveillance and data analysis, often need access to substantial quantities of personal information to operate efficiently. This engenders concerns over possible overreach when governments or law enforcement entities can violate individual private rights under the pretext of national security.[33] The deployment of AI-powered surveillance tools, such as facial recognition and biometric tracking, has sparked debates around the appropriate balance between public safety and individual freedoms. The continuous monitoring of individuals, especially across borders, can lead to concerns about the erosion of civil liberties, as well as the potential misuse of personal data. A significant difficulty is the presence of bias in machines that use AI. AI algorithms are developed using extensive datasets that may have inherent biases indicative of social disparities, including those associated with race, ethnicity, or socio-economic position. Failure to identify and rectify these biases may result in AI perpetuating discriminatory practices within law enforcement, perhaps causing the over-policing of certain areas or the erroneous targeting of people based on prejudiced data. Biased AI decision-making in law enforcement may significantly undermine public faith in the justice system, particularly in instances when AI is used for crime prediction or suspect profiling. Furthermore, the issue of accountability is significant in the use of AI in law enforcement. Determining responsibility for errors made by an AI system, like misidentifying a suspect or misinterpreting data, may be difficult, including developers, operators, or the system itself.[34] These legal ambiguities make it difficult to establish clear lines of accountability, complicating efforts to regulate AI’s role in crime prevention and investigation. This issue is compounded by the fact that AI systems often operate autonomously, making decisions based on algorithms that may not always be transparent or easily understood by human operators.[35] Internationally, the use of AI in transnational crime prevention is further complicated by jurisdictional and regulatory inconsistencies. Criminal networks operate across borders, yet legal frameworks governing the use of AI vary significantly between countries.[36] This lack of harmonization poses a challenge for cross-border cooperation, as differing standards on data sharing, privacy protections, and AI governance can create obstacles to effective collaboration between nations. Countries may have difficulties in harmonizing their legislation regarding AI use in the judicial system, particularly in ensuring that ethical issues, such as the safeguarding of human rights, are consistently maintained.[37] To ensure that AI is used responsibly and effectively in combating transnational crime, these ethical and legal challenges must be addressed.

Technological Limitations and Future Innovations in AI

Despite its impressive capabilities, Artificial Intelligence in law enforcement faces several technological limitations that can hinder its effectiveness in combating transnational crime.[38] A notable constraint is the quality and accessibility of data. Machine learning techniques depend significantly on extensive, precise datasets for optimum performance. Nonetheless, in many cases, the data accessible to law enforcement is inadequate, obsolete, or erroneous, hence diminishing the credibility of AI-generated insights. Criminal networks, particularly those functioning transnationally, often excel at concealing their digital and financial traces, complicating the ability of AI systems to identify trends or forecast actions due to inadequate data.[39] Additionally, AI systems can struggle to adapt to the rapidly evolving tactics of transnational criminals. Criminals constantly develop new strategies to evade detection, whether through exploiting technological loopholes or adopting countermeasures specifically designed to confuse AI-driven surveillance systems. This dynamic “arms race” between law enforcement and criminal organizations requires AI technologies to continually evolve, but current systems are not always flexible enough to anticipate these changes in real time.[40] Moreover, AI systems are not immune to error. While they excel at processing large amounts of data and identifying patterns, they can also misinterpret information or generate false positives, especially when faced with ambiguous or incomplete data. These errors can have serious consequences, such as misidentifying innocent individuals or overlooking critical threats. Human oversight is still crucial to ensure that AI-generated insights are properly contextualized and acted upon correctly. Without it, AI systems risk becoming over-reliant on automated processes that may not always reflect the nuances of human behavior or the complexity of transnational crime.[41]

Looking to the future, innovations in AI technology hold the potential to address these limitations. Advances in machine learning, quantum computing, and data encryption could significantly enhance the ability of AI systems to process information faster and with greater accuracy. For instance, quantum computing could vastly improve AI’s capacity to analyze encrypted data or detect cybercrimes that currently evade conventional computing systems.[42] Additionally, innovations in blockchain technology may improve the traceability of financial transactions, making it more difficult for criminal organizations to hide illicit activities.[43] However, these advancements also introduce new challenges, as criminal networks may exploit emerging technologies to stay ahead of law enforcement. As such, continuous innovation in AI must be paired with ongoing investment in cybersecurity and real-time monitoring tools to ensure that law enforcement agencies can effectively respond to evolving threats. Ultimately, the future success of AI in combating transnational crime will depend on the ability of these technologies to adapt, innovate, and integrate seamlessly with human oversight, ensuring that they remain a step ahead of increasingly sophisticated criminal organizations.[44]

Evaluation

The primary objective of this study was to assess the transformative potential of artificial intelligence (AI) in the detection, prevention, and eradication of transnational crime. Through the exploration of key AI technologies such as machine learning, predictive analytics, and biometric systems, the research highlighted the practical applications of these innovations in combating complex criminal activities, including human trafficking, cybercrime, terrorism, and drug smuggling. The findings revealed that AI has significantly enhanced law enforcement’s ability to process vast amounts of data, identify patterns of criminal behavior, and predict future crimes. In particular, AI’s role in predictive policing and real-time surveillance has allowed law enforcement agencies to act more proactively, disrupting criminal activities before they fully materialize. This predictive capability is a game-changer in transnational crime, where criminal networks operate across borders, often under the radar of traditional law enforcement techniques.

The assessment of AI’s efficacy in these domains highlighted several ethical, functional, and constitutional issues that must be resolved to guarantee its responsible use.[45] A primary obstacle noted was the concern about data privacy. The research validated that AI systems often need access to extensive datasets, potentially including personal and sensitive information, hence raising issues over possible overreach and the violation of human rights. The study emphasized the need for explicit legal frameworks that dictate the use of AI technology, especially for data collecting, storage, and analysis. Moreover, the issue of algorithmic bias was a significant concern.[46] The study found that AI systems trained on biased datasets could disproportionately target certain racial or socio-economic groups, leading to unequal treatment in law enforcement and undermining public trust. As AI continues to be integrated into global security efforts, addressing these biases and ensuring fairness in AI decision-making will be critical to maintaining the integrity of justice systems.

Another important objective of the study was to evaluate the need for international cooperation in the use of AI for law enforcement. The research highlighted the complexities of combating transnational crime, which often requires cross-border collaboration and intelligence sharing between countries. However, the study revealed that the lack of harmonized regulations and differing legal frameworks across nations can create obstacles to effective cooperation.[47] For AI to reach its full potential in combating transnational crime, countries must work together to develop standardized protocols for data sharing and AI governance. This collaborative approach will not only improve the efficiency of AI systems but also ensure that they are used ethically and within the bounds of international law.

In addition to the ethical and legal challenges, the study also evaluated the technological limitations of AI in law enforcement. While AI has demonstrated impressive capabilities in processing data and detecting criminal activity, it is not infallible.[48] The research found that AI systems are only as effective as the data they are trained on, and in many cases, law enforcement agencies have incomplete or outdated datasets, which can lead to errors in AI predictions or analysis. Furthermore, the study emphasized that criminal networks are continuously evolving, developing new methods to evade detection by AI systems.[49] This ongoing “arms race” between law enforcement and criminals requires continuous innovation in AI technologies. The study concluded that for AI to remain effective in the long term, it must adapt to the dynamic nature of transnational crime, and law enforcement agencies must invest in both technological advancements and human oversight to interpret AI-generated insights accurately.

RECOMMENDATIONS

To fully harness the power of Artificial Intelligence in eradicating transnational crime, a multi-faceted approach is essential—one that addresses the complexities of global crime networks while safeguarding ethical and legal standards.[50] The first and perhaps most pressing recommendation is to foster deeper international cooperation. Transnational crime, by its very nature, operates across borders, exploiting gaps in jurisdiction and legal enforcement.[51] AI, though a powerful tool for law enforcement, can only be truly effective if nations work together to share data, intelligence, and best practices. A critical challenge in combating transnational crime is the disparity in technological capabilities and legal frameworks among different countries. While some nations are advanced in deploying AI for law enforcement, others may lack the resources or regulatory infrastructure to do so effectively. To overcome these challenges, countries must establish standardized protocols for sharing AI-driven intelligence, ensuring that critical data can be accessed and analyzed seamlessly across borders.[52] International organizations such as Interpol and the United Nations Office on Drugs and Crime (UNODC) can play a key role in facilitating this cooperation, helping to create collaborative platforms that enable law enforcement agencies worldwide to leverage AI tools more effectively. Such initiatives would not only increase the scope and reach of AI technologies but also enhance the global community’s ability to respond to the evolving threat of transnational crime.[53]

In addition to promoting collaboration, it is crucial to develop strong ethical and regulatory frameworks that regulate the use of AI in law enforcement. Although AI has unparalleled powers in surveillance, data analysis, and crime forecasting, its use engenders substantial issues over privacy, responsibility, and equity. AI systems, especially those used for surveillance, sometimes need access to extensive personal data, provoking concerns around overreach and possible abuses by law enforcement or governmental entities.[54] To address these issues, extensive legal frameworks must be established to control the deployment of AI technology, ensuring their usage respects individual civil freedoms and human rights. This entails formulating explicit protocols for data gathering, storage, and use, alongside instituting stringent measures for supervision and accountability. Independent monitoring entities must be established to scrutinize the use of AI in law enforcement, guaranteeing openness and safeguarding against any abuses.[55] These bodies would also be responsible for ensuring that AI algorithms are free from bias, particularly in relation to race, ethnicity, religion and socio-economic status. Research has shown that AI systems can reflect and even amplify societal biases if they are trained on skewed datasets, leading to discriminatory outcomes in law enforcement.[56] As AI increasingly integrates into global crime-fighting initiatives, it is crucial to establish safeguards that prevent biases from compromising the fairness and equality of the judicial system.

More so, investing in the ongoing development of AI technology is essential for augmenting its effectiveness in addressing international crime. Criminal networks continuously evolve, devising new strategies and technology to avoid detection and exploit weaknesses in law enforcement systems. To remain proactive against these ever-evolving dangers, AI technologies must advance accordingly. Governments and private sector organizations must provide resources for research and development to guarantee that AI systems stay at the forefront of innovation.[57] This includes the enhancement of machine learning methods, predictive analytics, and cybersecurity measures. AI systems that analyze real-time data from many sources—such as social media, financial transactions, and communication networks—can assist law enforcement authorities in anticipating and preventing illegal behaviors before they happen. Moreover, nascent innovations like quantum computing and blockchain may augment AI’s ability to identify and deconstruct intricate criminal networks, especially in domains like financial crime and cybercrime. Nonetheless, the rapidity of invention presents additional concerns, since criminal groups may endeavor to use these technical developments for their benefit.[58] This underscores the importance of ongoing investment not only in AI development but also in cybersecurity and counterintelligence measures to safeguard against criminal exploitation of advanced technologies.

In addition to technological innovation, capacity-building within law enforcement agencies is critical to the successful integration of AI. AI tools, while powerful, are only as effective as the people who use them. Law enforcement personnel need specialized training to understand how to operate AI systems, interpret AI-generated insights, and make informed decisions based on the data provided by these systems.[59] Investing in the professional development of officers and analysts ensures that AI technologies are used to their full potential. Moreover, training programs should emphasize the ethical considerations of AI deployment, ensuring that law enforcement personnel are aware of the risks associated with algorithmic bias, data privacy, and civil liberties.[60] By equipping law enforcement with the skills and knowledge needed to navigate the complexities of AI, agencies can not only improve their operational efficiency but also foster public trust in the responsible use of AI technologies.[61]

It is also essential to strike a delicate balance between innovation and ethical responsibility. While AI holds tremendous promise in transforming global crime prevention, it must be deployed in a manner that respects human rights and upholds the rule of law. Policymakers, technologists, and law enforcement agencies must work collaboratively to create AI solutions that are both effective and fair. This means designing AI systems that are transparent, accountable, and subject to rigorous oversight. It also involves ensuring that AI is used to support, rather than replace, human decision-making in law enforcement. AI should be viewed as a tool to enhance the capabilities of law enforcement, not as a substitute for human judgment and ethical consideration. Moreover, the use of AI in law enforcement should always be guided by a commitment to protecting individual freedoms and promoting justice. By prioritizing ethical considerations alongside technological advancements, AI can become an indispensable tool in the fight against transnational crime, while maintaining the integrity and fairness of the justice system.[62]

Suffice it to say that enhancing AI’s role in eradicating transnational crime requires a comprehensive approach that includes fostering international cooperation, developing ethical and legal frameworks, investing in continuous innovation, building capacity within law enforcement, and ensuring that AI technologies are used responsibly. Through these strategies, AI can become a transformative force in global security, offering new possibilities for combating the complex and evolving threat of transnational crime while upholding the principles of justice and equality.

CONCLUSION

In conclusion, the role of Artificial Intelligence in eradicating transnational crime has the potential to transform global law enforcement by providing sophisticated tools for detecting, preventing, and dismantling complex criminal networks. AI’s ability to analyze vast amounts of data in real-time, predict criminal behavior, and enhance surveillance has already proven effective in addressing major challenges posed by cybercrime, human trafficking, drug smuggling, and terrorism. However, alongside these advancements come critical ethical, legal, and operational challenges that must be addressed. Issues such as privacy concerns, algorithmic bias, accountability, and the need for international cooperation highlight the complexities of deploying AI responsibly in law enforcement. As criminals evolve and exploit technological advancements, the need for continuous innovation and adaptation in AI technologies is essential. Yet, this innovation must be accompanied by strong regulatory frameworks and oversight mechanisms to ensure that AI is used ethically, transparently, and without infringing on civil liberties. The future success of AI in eradicating transnational crime depends not only on technological breakthroughs but also on the ability of nations to work together, build trust, and integrate these tools in a manner that balances security with the protection of human rights. Ultimately, AI holds the promise of revolutionizing global crime prevention, but its effectiveness will rely on responsible governance and the commitment to maintaining fairness and justice at the forefront of its application.

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FOOTNOTES

[1] Faiyaz, T., & Sidhu, B. K. (2024). Tackling IUU Fishing, Transnational Organized Crime (TOC) and Maritime Security Threats in the Bay of Bengal: The Role of India and Regional Cooperation Under International Law. J. Territorial & Mar. Stud., 11, 43.

[2] Bloom, D. E., & Cadarette, D. (2019). Infectious disease threats in the twenty-first century: strengthening the global response. Frontiers in immunology, 10, 549.

[3] Hassan, M., Aziz, L. A. R., & Andriansyah, Y. (2023). The role artificial intelligence in modern banking: an exploration of AI-driven approaches for enhanced fraud prevention, risk management, and regulatory compliance. Reviews of Contemporary Business Analytics, 6(1), 110-132.

[4] Papadouka, M. E. Uncovering Trends in Human Trafficking and Migrant Smuggling Activities: A Natural Language Processing Approach to.

[5] Fletcher, R. R., Nakeshimana, A., & Olubeko, O. (2021). Addressing fairness, bias, and appropriate use of artificial intelligence and machine learning in global health. Frontiers in artificial intelligence, 3, 561802.

[6] Hufnagel, S. (2016). Policing cooperation across borders: Comparative perspectives on law enforcement within the EU and Australia. Routledge.

[7] Grimm, P. W., Grossman, M. R., & Cormack, G. V. (2021). Artificial intelligence as evidence. Nw. J. Tech. & Intell. Prop., 19, 9.

[8] Johnson, J. (2019). Artificial intelligence & future warfare: implications for international security. Defense & Security Analysis, 35(2), 147-169.

[9] Peters, K. M. (2019). 21st century crime: How malicious artificial intelligence will impact homeland security (Doctoral dissertation, Monterey, CA; Naval Postgraduate School).

[10] Palme, C. C. (2024). From The Deceptive Delinquent To The Illusive Illicit Alien: A Qualitative Study Of 21st Century United States Border Security Law Enforcement’s Capabilities, Competencies, And Capacities Designed To Counter Transient Criminality Recruitment.

[11] King, T. C., Aggarwal, N., Taddeo, M., & Floridi, L. (2020). Artificial intelligence crime: An interdisciplinary analysis of foreseeable threats and solutions. Science and engineering ethics, 26, 89-120.

[12] Mishra A., Kahla L. Z., Gayflor N. (2024). Leveraging Artificial Intelligence for Crime Detection and Prevention. Retrieved from https://www.researchgate.net/publication/381295463_Leveraging_Artificial_Intelligence_for_Crime_Detection_and_Prevention

[13] Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774.

[14] Peter Sloly. (2018) Emerging tech that can make smart cities safer. Deloitte

[15] Patil S., Gund P., and Phalke V. (2023). Investigating Crime: A Role of Artificial Intelligence in Criminal Justice. Retrieved from https://www.researchgate.net/publication/371415843_INVESTIGATING_CRIME_A_ROLE_OF_ARTIFICIAL_INTELLIGENCE_IN_CRIMINAL_JUSTICE

[16] Potla, R. T. (2023). AI in Fraud Detection: Leveraging Real-Time Machine Learning for Financial Security. Journal of Artificial Intelligence Research and Applications, 3(2), 534-549.

[17] Begishev I., Asli M. R., Denisovich V., Mayorov A. (2023). Research of artificial intelligence as a subject of crime Retrieved from https://www.researchgate.net/publication/375744625_Research_of_artificial_intelligence_as_a_subject_of_crime Ildar, B., Mehrdad, R., Veronika, D., Andrey, M., Andrey, S (2023). Research of artificial intelligence as a subject of crime.

[18] Usman, H., Tariq, I., & Nawaz, B. (2023). In The Realm of The Machines: Ai’s Influence Upon International Law and Policy. Journal of Social Research Development, 4(2), 383-399.

[19] Morandín-Ahuerma, F. Recommendation of the OECD council on artificial intelligence: inequality and inclusion1.

[20] OECD.AI (2021), powered by EC/OECD (2021), database of national AI policies, accessed on 2/11/2024, https://oecd.ai.

[21] Lucia Russo, Noah Oder. (2023). How countries are implementing the OECD Principles for Trustworthy AI. https://oecd.ai/en/wonk/national-policies-2

[22] Hoofnagle, C. J., Van Der Sloot, B., & Borgesius, F. Z. (2019). The European Union general data protection regulation: what it is and what it means. Information & Communications Technology Law, 28(1), 65-98.

[23] Faracik, B. (2017). Implementation of the UN Guiding Principles on Business and Human Rights. European Parliament, PE, 578.

[24] International Telecommunication Union. (2017). AI for Good Global Summit. Retrieved from https://www.itu.int/en/ITU-T/AI/Pages/201706-default.aspx

[25] Yilma, K. (2024). African AI Ethics? —The Role of AI Ethics Initiatives in Africa. The Role of AI Ethics Initiatives in Africa (May 01, 2024).

[26] King, T. C., Aggarwal, N., Taddeo, M., & Floridi, L. (2020). Artificial intelligence crime: An interdisciplinary analysis of foreseeable threats and solutions. Science and engineering ethics, 26, 89-120.

[27] Ijiga, A. C., Olola, T. M., Enyejo, L. A., Akpa, F. A., Olatunde, T. I., & Olajide, F. I. (2024). Advanced surveillance and detection systems using deep learning to combat human trafficking. Magna Scientia Advanced Research and Reviews, 11(1), 267-286.

[28] Barney, D. (2018). Trafficking Technology: A look at different approaches to ending technology-facilitated human trafficking. Pepp. L. Rev., 45, 747.

[29] Khan, M. I., Arif, A., & Khan, A. (2024). The Most Recent Advances and Uses of AI in Cybersecurity. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 566-578.

[30] Osman, K. (2024). Improving Transparency with Technology in the Transportation of Illegal Wildlife.

[31] Duncan, K. A. (2020). Role of Intelligence in the Prevention of Terrorism (early Warning-Early Response). International Centre for Counter-Terrorism (ICCT).

[32] Çelikkaya, D. (2024). Ethical Concerns of Artificial Intelligence use in the Criminal Justice System Under EU Law (Master’s thesis, Marmara Universitesi (Turkey)).

[33] Donohue, L. K. (2015). High technology, consumer privacy, and US national security. Am. U. Bus. L. Rev., 4, 11.

[34] Pasquale, F. (2019). Data-informed duties in AI development. Colum. L. Rev., 119, 1917.

[35] Zerilli, J., Knott, A., Maclaurin, J., & Gavaghan, C. (2019). Transparency in algorithmic and human decision-making: is there a double standard? Philosophy & Technology, 32, 661-683.

[36] Velasco, C. (2022, May). Cybercrime and Artificial Intelligence. An overview of the work of international organizations on criminal justice and the international applicable instruments. In ERA Forum (Vol. 23, No. 1, pp. 109-126). Berlin/Heidelberg: Springer Berlin Heidelberg.

[37] Aloamaka, P. C., & Omozue, M. O. (2024). AI and Human Rights: Navigating Ethical and Legal Challenges in Developing Nations. Khazanah Hukum, 6(2), 189-201.

[38] Faqir, R. S. (2023). Digital Criminal Investigations in the Era of Artificial Intelligence: A Comprehensive Overview. International Journal of Cyber Criminology, 17(2), 77-94.

[39] Pappachan, P., Adi, N. S., & Firmansyah, G. (2024). Deep Learning-Based Forensics. Digital Forensics and Cyber Crime Investigation: Recent Advances and Future Directions, 211.

[40] Scharre, P. (2019). Killer apps: The real dangers of an AI arms race. Foreign Aff., 98, 135.

[41] Cheng, L., Varshney, K. R., & Liu, H. (2021). Socially responsible ai algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137-1181.

[42] Brooks, C. (2024). Inside Cyber: How AI, 5G, IoT, and Quantum Computing Will Transform Privacy and Our Security. John Wiley & Sons.

[43] Utkina, M. (2023). Leveraging Blockchain Technology for Enhancing Financial Monitoring: Main Challenges and Opportunities. European Journal of Interdisciplinary Studies, 15(2).

[44] Dhabu, A. C. (2024). Legal Implications of Artificial Intelligence in Cross-Border Transactions.

[45] Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., de Prado, M. L., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy Artificial Intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, 101896.

[46] Patel, K. (2024). Ethical reflections on data-centric AI: balancing benefits and risks. International Journal of Artificial Intelligence Research and Development, 2(1), 1-17.

[47] Kurowska-Pysz, J., & Szczepańska-Woszczyna, K. (2017). The analysis of the determinants of sustainable cross-border cooperation and recommendations on its harmonization. Sustainability, 9(12), 2226.

[48] Hayward, K. J., & Maas, M. M. (2021). Artificial intelligence and crime: A primer for criminologists. Crime, Media, Culture, 17(2), 209-233.

[49] Sarkar, G., & Shukla, S. K. (2023). Behavioral analysis of cybercrime: Paving the way for effective policing strategies. Journal of Economic Criminology, 100034.

[50] Saeed, J. (2024). Guardians of the Data: Government Use of AI and IoT in the Digital Age.

[51] Luban, D., O’Sullivan, J. R., Stewart, D. P., & Jain, N. (2023). International and transnational criminal law. Aspen Publishing.

[52] Dhabu, A. C. (2024). Legal Implications of Artificial Intelligence in Cross-Border Transactions.

[53] King, T. C., Aggarwal, N., Taddeo, M., & Floridi, L. (2020). Artificial intelligence crime: An interdisciplinary analysis of foreseeable threats and solutions. Science and engineering ethics, 26, 89-120.

[54] Eze, G. (2023). Ethical Considerations in the Deployment of AI Surveillance During Public Health Crises. Journal of Innovative Technologies, 6(1).

[55] Busuioc, M. (2022). AI algorithmic oversight: new frontiers in regulation. In Handbook of Regulatory Authorities (pp. 470-486). Edward Elgar Publishing.

[56] Scatiggio, V. (2020). Tackling the issue of bias in artificial intelligence to design ai-driven fair and inclusive service systems. How human biases are breaching into ai algorithms, with severe impacts on individuals and societies, and what designers can do to face this phenomenon and change for the better.

[57] Mhlanga, D. (2021). Artificial intelligence in the industry 4.0, and its impact on poverty, innovation, infrastructure development, and the sustainable development goals: Lessons from emerging economies?. Sustainability, 13(11), 5788.

[58] Wall, D. S. (2024). Cybercrime: The transformation of crime in the information age. John Wiley & Sons.

[59] Ejjami, R. (2024). AI-driven justice: Evaluating the impact of artificial intelligence on legal systems. Int. J. Multidiscip. Res, 6(3), 1-29.

[60] Zuwanda, Z. S., Lubis, A. F., Solapari, N., Sakmaf, M. S., & Triyantoro, A. (2024). Ethical and Legal Analysis of Artificial Intelligence Systems in Law Enforcement with a Study of Potential Human Rights Violations in Indonesia. The Easta Journal Law and Human Rights, 2(03), 176-185.

[61] Apostolakis, K. C., Dimitriou, N., Margetis, G., Ntoa, S., Tzovaras, D., & Stephanidis, C. (2021). DARLENE–Improving situational awareness of European law enforcement agents through a combination of augmented reality and artificial intelligence solutions. Open Research Europe, 1.

[62] Çelikkaya, D. (2024). Ethical Concerns of Artificial Intelligence use in the Criminal Justice System Under EU Law (Master’s thesis, Marmara Universitesi (Turkey)).