AI AND THE FUTURE OF ANTI-CORRUPTION GOVERNANCE

Corruption remains a persistent global problem, and many Central Asian states have demonstrated limited advancement or even deterioration in their efforts to address it. Although Kazakhstan and Uzbekistan have achieved measurable progress, countries such as Turkmenistan, Tajikistan, and Kyrgyzstan have seen notable setbacks, highlighting ongoing institutional and governance deficiencies. This study emphasizes the growing relevance of artificial intelligence as a potential instrument in anti-corruption initiatives. Through capabilities such as large-scale data analysis, anomaly detection, risk prediction, and the automation of bureaucratic procedures, AI can help minimize opportunities for misconduct and strengthen transparency. The paper also examines Uzbekistan‘s current anti-corruption reforms, particularly the digitalization of administrative processes, while noting that the integration of advanced AI tools remains at an early stage. Nevertheless, the adoption of AI introduces its own risks, including algorithmic bias, data privacy issues, and the possibility of exploitation by authorities. To be effective, AI-driven anti-corruption frameworks must be supported by robust governance mechanisms, openness, accountability, and ongoing evaluation. Ultimately, the research stresses that meaningful cooperation between state institutions, civil society organizations, and the private sector is crucial to fully realize AI‘s potential while ensuring ethical, equitable, and trustworthy approaches to fighting corruption.

TSUL LEGAL REPORT INTERNATIONAL ELECTRONIC SCIENTIFIC JOURNAL

JOURNAL DOI: 10.51788/tsul.lr. ISSUE DOI: 10.51788/tsul.lr.7.1. E-ISSN: 2181-1024

Founder: Tashkent State University of Law

INTERNATIONAL ELECTRONIC SCIENTIFIC JOURNAL VOLUME 7 ISSUE 1 MARCH 2026

JOURNAL DOI: 10.51788/tsul.lr. ISSUE DOI: 10.51788/tsul.lr.7.1. «TSUL Legal Report» international electronic scientific journal was registered by the Press and Information Agency of Uzbekistan on April 21, 2020, with certificate number 1342.The journal is included in the list of journals of the Higher Attestation Commission under the Ministry of Higher Education, Science and Innovations of the Republic of Uzbekistan. Copyright belongs to Tashkent State University of Law. All rights reserved. Use, distribution and reproduction of materials of the journal are carried out with the permission of the founder.

Editor: Elyor Mustafaev Graphic designer: Umid Sapaev Editorial office address: Tashkent, st. Sayilgoh, 35. Index 100047. Principal Contact Tel.: ([phone number hidden] Fax: ([phone number hidden] Website: legalreport.tsul.uz E-mail: info@legalreport.tsul.uz Publishing license № 174625, 29.11.2023. E-ISSN: 2181-1024

© 2026. TSUL – Tashkent State University of Law.

EDITOR–IN–CHIEF B. Khodjaev – Rector of Tashkent State University of Law, Doctor of Law, Professor

Z. Esanova – Deputy Rector for Scientific Affairs and Innovations of Tashkent State University of Law, Doctor of Law, Professor

E. Mustafaev – Head of the Periodicals Department of the Editorial and Publishing Center of Tashkent State University of Law

N. Koutras – Kurtin Law School, PhD (Australia) H. Yeung – Associate Professor of Leicester Law School, University of Leicester, (UK) C. Kelley – Associate Professor of Law School of Arkansas University (USA) A. Younas – Researcher of the Chinese Academy of Sciences, General Director of Ai Mo Innovation Consultants (China) Sh. Al-Fatih – Lecturer of the University of Muhammadiyah Malang (Indonesia) A. Jaelani – Lecturer of Sebelas Maret University (Indonesia) N. Upadhyay – Associate Professor of the Department of Law and Programs of the Symbiosis Law School (India) Y. Ogurlu – Rector of Balıkesir University (Turkey) K. Molodyko – Chief Expert of the Institute of National and Comparative Law Research at the “School of Higher Economics” of the National Research University of Russia, Associate Professor of the Department of International Law (Russia)

N. AllahRakha – Lecturer of the Department of Cyber Law of Tashkent State University of Law, Doctor of Philosophy (PhD) in Law (Pakistan)

N. Rakhmonkulova – Professor of the Department of Private International Law of Tashkent State University of Law, Doctor of Law (Uzbekistan) A. Tadjibaeva – Associate Professor of the Department of Criminal Procedure Law of Tashkent State University of Law, Candidate of Legal Sciences (Uzbekistan) R. Urinboev – Professor of the Department of Sociology of Law of Lund University, Doctor of Law (Austria) M. Kudratov – Doctor of the University of Regensburg (Germany) Kh. Radjapov – Associate Professor of the Department of Business Law of Tashkent State University of Law, Doctor of Philosophy (PhD) in Law (Uzbekistan) O. Narziev – Professor of the Department of Private International Law of Tashkent State University of Law, Doctor of Law (Uzbekistan) Sh. Rahmonov – Head of the Department of International Law and Human Rights of Tashkent State University of Law, Doctor of Law, Professor (Uzbekistan)

Volume 7, Issue 1 (2026) E-ISSN: 2181-1024

12.00.01 – THEORY AND HISTORY OF STATE AND LAW. HISTORY OF LEGAL DOCTRINES ESHTURDIYEVA FARANGIZ BAKHODIR KIZI A comparative legal analysis of national legal policy models ...............................................4

12.00.05 – LABOR LAW. SOCIAL SECURITY LAW RAKHIMOV MIRYOQUB AKTAMOVICH Fundamental principles of labor law of the Kyrgyz Republic ............................................. 13 AZIMOV AZIMBEK AZIZBEK UGLI The importance of introducing regional “Roadmaps” in supporting youth employment .... 22

12.00.06 – THE LAW OF NATURAL RESOURCES. AGRARIAN LAW. ENVIRONMENTAL LAW ABDUSAMADOVA ZARINA SHOBIDDIN KIZI Legal regulation, practice, and systematic analysis of land servitude in the legislation of Uzbekistan and CIS countries ....................................................................................... 30

12.00.07 – JUDICIAL BRANCH. PROSECUTOR’S CONTROL. ORGANIZATION OF LAW ENFORCEMENT. ADVOCACY SOBITOVA UMIDAKHON MUROD KIZI General supervision in the mechanism of ensuring legality: the theoretical and legal aspect of prosecutorial activity .................................................................................. 38

12.00.08 – CRIMINAL LAW. CRIMINAL-EXECUTIVE LAW ACHILOV IZZAT TULKINOVICH Causes of crimes against the sexual inviolability of minors and conditions contributing to their commission ........................................................................................ 46

12.00.09 – CRIMINAL PROCEEDINGS. FORENSICS, INVESTIGATIVE LAW AND FORENSIC EXPERTISE SULTONOVA LOBAR MAMARAYIM KIZI Extradition as a mechanism of the inevitability of criminal liability .................................... 54

12.00.10 – INTERNATIONAL LAW TILLABOEV SHOHRUKHBEK MIRZATILLO UGLI The challenges of legal regulation of human rights in the digital age ................................ 67

MUSASHAYKHOV KARIMJON KHUSANXANOVICH Analysis of corruption factors related to match-fixing in professional sports ..................... 78 AHMADJONOV MURODULLO NURALI UGLI AI and the future of anti-corruption governance ................................................................ 83

E-ISSN: 2181-1024 Volume 7, Issue 1 (2026) TSUL Legal Report 3

TSUL LEGAL REPORT Journal home page: www.legalreport.tsul.uz

Received: 09.02.2026 Accepted: 16.03.2026 Published: 30.03.2026

DOI: https://dx.doi.org/10.51788/tsul.lr.7.1./EMKR2443

AI AND THE FUTURE OF ANTI-CORRUPTION GOVERNANCE Ahmadjonov Murodullo Nurali ugli, Assistant to the Prosecutor of Kosonsoy district, Namangan region ORCID: https://orcid.org/0009-0006-4286-6752 e-mail: murodulloahmadjonov123@gmail.com

Abstract. Corruption remains a persistent global problem, and many Central Asian states have demonstrated limited advancement or even deterioration in their efforts to address it. Although Kazakhstan and Uzbekistan have achieved measurable progress, countries such as Turkmenistan, Tajikistan, and Kyrgyzstan have seen notable setbacks, highlighting ongoing institutional and governance deficiencies. This study emphasizes the growing relevance of artificial intelligence as a potential instrument in anti-corruption initiatives. Through capabilities such as large-scale data analysis, anomaly detection, risk prediction, and the automation of bureaucratic procedures, AI can help minimize opportunities for misconduct and strengthen transparency. The paper also examines Uzbekistan’s current anti-corruption reforms, particularly the digitalization of administrative processes, while noting that the integration of advanced AI tools remains at an early stage. Nevertheless, the adoption of AI introduces its own risks, including algorithmic bias, data privacy issues, and the possibility of exploitation by authorities. To be effective, AI-driven anti-corruption frameworks must be supported by robust governance mechanisms, openness, accountability, and ongoing evaluation. Ultimately, the research stresses that meaningful cooperation between state institutions, civil society organizations, and the private sector is crucial to fully realize AI’s potential while ensuring ethical, equitable, and trustworthy approaches to fighting corruption. Keywords: artificial intelligence, administrative oversight, discretionary power, anti-corruption efforts, political malpractice, legal gaps, digital governance algorithmic accountability

Introduction Corruption continues to rank among the most serious global issues, eroding the quality of governance, weakening public confidence, and constraining economic growth. Across much of Asia, efforts to curb corruption have shown minimal advancement and, in some cases, clear deterioration, as reflected in Transparency International’s Corruption Perceptions Index. Kazakhstan and Uzbekistan stand out as relative success cases, recording tangible improvements, whereas Turkmenistan, Tajikistan, and Kyrgyzstan have

E-ISSN: 2181-1024 Volume 7, Issue 1 (2026) TSUL Legal Report 83

experienced pronounced declines, underscoring deep-rooted and persistent governance shortcomings. Conventional anti-corruption approaches such as audits, legal sanctions, and bureaucratic supervision often fail to adapt to increasingly complex and sophisticated forms of corruption, resulting in ongoing deficiencies in transparency and accountability (Ahmadjonov, 2024). Against this backdrop, artificial intelligence (AI) has gained attention as a potentially transformative instrument for enhancing anti-corruption strategies. Broadly understood as technologies capable of interpreting complex data environments and independently pursuing defined objectives, AI enables large-scale data processing, identification of irregular patterns, prediction of corruption risks, and automation of administrative functions. While the integration of Artificial Intelligence into public administration promises a paradigm shift in governance, it remains a subject of intense debate. The transition involves balancing significant operational gains against emerging societal risks. These include concerns over personal data protection, algorithmic bias, and the potential manipulation of AI systems by political actors. Accordingly, this study explores the expanding use of AI in anti-corruption policies, its role within public governance frameworks, and the ethical and operational principles necessary to ensure its responsible and effective application on the whole. Methods This research adopts a qualitative and comparative approach to shed light on the use of artificial intelligence in anti-corruption governance, with particular emphasis on Uzbekistan. It is based on the examination of official reports, policy documents, and scholarly sources to identify corruption patterns and institutional developments. The study places Uzbekistan in a broader Central Asian context through regional comparison. A case study analysis investigates sector-specific vulnerabilities, reform efforts, and digital transformation measures. In addition, the research evaluates the legal, ethical, and institutional dimensions of AI implementation, focusing on regulatory frameworks, oversight mechanisms, transparency standards, and administrative capacity to assess its role in reinforcing public integrity. Results The results indicate that corruption continues to be prevalent throughout Asia, with most countries demonstrating little advancement and, in some cases, noticeable decline. Apart from a small number of states, most prominently Kazakhstan and Uzbekistan, progress remains limited, pointing to enduring institutional and governance deficiencies. The findings further suggest that the systematic integration of artificial intelligence can significantly enhance anti-corruption initiatives. Where implemented effectively, AI-driven tools have strengthened the identification and anticipation of corruption risks, particularly in areas such as public procurement, contractual processes, and customs operations, by uncovering irregular patterns and inefficiencies that conventional monitoring mechanisms frequently overlook (Ahmadjonov, 2025a). Empirical evidence from public-sector implementations, including Spain’s Saler platform, illustrates how AI contributes to preventive governance by enabling extensive data processing and early-stage risk detection. In the case of Uzbekistan, corruption is most evident within the healthcare and education sectors, as well as at the local government level, with a substantial proportion of cases involving assistant governors. Recent policy measures aimed at limiting individual discretion through administrative digitalization and collective decision-making structures are expected to mitigate these risks. Nevertheless, the analysis also draws attention to important constraints, including biased datasets, insufficient transparency, and inadequate oversight mechanisms.

84 TSUL Legal Report Volume 7, Issue 1 (2026) E-ISSN: 2181-1024

Notwithstanding these challenges, AI enhances the efficiency and precision of anti-corruption measures, although its effectiveness ultimately relies on robust governance arrangements, reliable data inputs, and continuous human supervision. Discussion Corruption continues to represent one of the most critical challenges facing the global community. Data from Transparency International’s Corruption Perceptions Index indicate that across much of Asia, efforts to address corruption have yielded minimal progress, with several countries experiencing clear setbacks. Kazakhstan and Uzbekistan emerge as notable outliers, having achieved discernible improvements, whereas Turkmenistan, Tajikistan, and Kyrgyzstan have deteriorated markedly, ranking 17th, 19th, and 25th from the lowest positions, respectively. These developments underscore entrenched governance shortcomings and reinforce the urgency of adopting innovative and more effective countermeasures (Alkhodary & Saidat, 2023). Within this context, artificial intelligence (AI) has gained prominence as a potentially transformative instrument in anti-corruption strategies. The European Commission’s High-Level Expert Group on Artificial Intelligence characterizes AI as systems capable of demonstrating intelligent behavior through environmental analysis and autonomous action to achieve defined goals. Applied to anti-corruption frameworks, AI enables the analysis of extensive datasets, detection of irregular patterns, anticipation of corruption risks, and automation of bureaucratic processes. However, the deployment of AI also introduces substantial ethical and political concerns, including risks to personal data protection, large-scale surveillance, algorithmic discrimination, and manipulation for political purposes, particularly in electoral settings (AllahRakha, 2023). Despite these concerns, the advantages of AI can outweigh its risks when supported by strong regulatory and ethical safeguards. The increasing integration of AI into anti-corruption initiatives often described as AI-assisted anti-corruption technologies (AI-ACT) has drawn growing interest from policymakers, civil society actors, investigative media, and academic researchers. As outlined by Mattoni, AI-ACT refers to sociotechnical systems that leverage large-scale data analysis to limit discretionary authority among public officials and enhance engagement between citizens and state institutions. By reducing individual decision-making power in corruption-prone domains, such systems can help eliminate persistent structural vulnerabilities that facilitate the misuse of authority. At the global level, AI’s forecasting and preventive functions have already contributed to faster and more effective anti-corruption interventions. Odilla identifies over thirty initiatives led by governmental bodies and civil society organizations that employ AI to observe, detect, report, and anticipate corruption-related misconduct. Although implementation often faces technical, institutional, and capacity-related obstacles, AI has demonstrated particular value in exposing irregularities in public procurement and contracting, overseeing tender processes, and detecting customs fraud-sectors historically susceptible to corrupt practices. Nonetheless, significant challenges remain unresolved. Institutional adaptation to increasingly complex corruption mechanisms is frequently slow, auditing resources are often insufficient, and AI systems require continuous monitoring and improvement (AllahRakha, 2024). Ensuring data quality, mitigating algorithmic bias, promoting responsible use, and correcting systemic flaws are essential to sustaining effectiveness while maintaining public confidence. AI Applications in the Public Sector In the public sector, generative artificial intelligence and large language models (LLMs) present new possibilities for enhancing transparency and strengthening institutional

E-ISSN: 2181-1024 Volume 7, Issue 1 (2026) TSUL Legal Report 85

oversight. One of their most significant contributions lies in advanced data processing and pattern recognition. AI technologies make it possible to analyze extensive datasets to uncover early indicators of corruption, including data inconsistencies, repeated entries, and atypical trends in financial flows, public procurement processes, contractual arrangements, and subsidy distribution schemes (Aritonang, 2017). By integrating historical records with real-time data, these systems can also anticipate which regions or sectors are at heightened risk of fraudulent activity. An illustrative case of preventive governance is the Saler Rapid Alert System, developed by the General Inspection of Services of the Generalitat Valenciana in Spain. Saler is designed to identify potential risks and organizational vulnerabilities stemming from inefficient practices or bureaucratic stagnation. It consolidates large volumes of digitized information from public administrations, official registries, notarial records, and intellectual property databases to evaluate administrative procedures relevant to compliance and oversight. The system highlights risk areas related to information security, public procurement, appointment committees, collusion, auditing processes, governance standards, ethical conduct, legal compliance, and human resource management (Barrington, 2022). In addition, Saler applies predictive modeling to detect potentially fraudulent unemployment benefit claims by estimating individuals’ health status and probability of re-entering the workforce. Such uses, however, require strict regulatory and procedural safeguards to minimize false positives and prevent undue harm to citizens who lawfully depend on public support mechanisms. Comparable AI-based tools can also be deployed to scrutinize spending at municipal and ministerial levels, identifying budget overruns, operational inefficiencies, and weak administrative practices. Given that public procurement consistently ranks among the sectors most vulnerable to corruption and that oversight bodies often face limited resources, AI functions as a valuable force multiplier in monitoring activities (Cardellini, 2023). Beyond surveillance and detection, AI supports anti-corruption objectives by automating routine administrative functions, reducing the likelihood of human error, and accelerating the identification of corruption-related risks. AI-enabled reporting and whistleblowing platforms can further streamline institutional procedures, improve resource deployment, and shorten investigative processes. In Uzbekistan, however, the adoption of such technologies remains at an early and largely experimental stage. Uzbekistan: A Case in Point Data released by Uzbekistan’s Anti-Corruption Agency indicate that exposure to corruption is highest within the healthcare and education sectors, as well as at the level of local public administration. From a geographic perspective, the greatest concentration of risk has been identified in Tashkent City, Tashkent Region, and the Fergana Valley (Cardellini, 2023). As emphasized by the Agency’s director, Akmal Burkhanov, an overwhelming share-estimated at between four-fifths and nine-tenths of corruption-related offenses involves assistant governors, pointing to a critical vulnerability within local governance structures. Official statistics further reveal a steady increase in recorded criminal activity. In 2023, authorities documented 5,222 offenses involving 6,373 individuals, while the following year saw the total number of crimes rise to 5,716. Although reported corruption cases grew in regions such as Fergana and Surkhandarya, notable declines were observed in Khorezm, Kashkadarya, Tashkent, and Samarkand. By 2025, the institutions most frequently associated with corruption investigations included the Ministries responsible for preschool

86 TSUL Legal Report Volume 7, Issue 1 (2026) E-ISSN: 2181-1024

and school education, the health sector, and district-level administrations (khokimiyats) (Carter & Belanger, 2005). At the subnational level, twelve district and city governors were prosecuted for corruption-related offenses, with more than three hundred local administrative bodies implicated overall. In response to these developments, the government has introduced reforms aimed at reducing discretionary decision-making in the allocation of subsidies and concessional loans (Carter et al., 2023). These measures include the adoption of collective approval mechanisms, the involvement of neighborhood-based councils, and the full digitalization of administrative procedures. According to Burkhanov, such reforms are expected to substantially curb corruption associated with assistant governors by 2026. The disproportionate concentration of corruption cases in key regions highlights the pressing need for a more systematic deployment of artificial intelligence across both public institutions and private-sector operations, particularly as a means of strengthening prevention, monitoring, and accountability mechanisms. Risks, Ethics, and the Limits of AI Notwithstanding the increasing optimism surrounding artificial intelligence, its implementation is accompanied by serious risks and vulnerabilities. In environments where oversight mechanisms are weak, public officials may deliberately exploit AI-based systems for private benefit, taking advantage of their technical opacity and limited external scrutiny. Excessive dependence on automated tools also raises significant ethical and legal questions. A case in point is Albania’s proposal to rely on AI, most notably ChatGPT, for the translation and incorporation of European Union legislation into domestic law (Hoa & Thanh, 2023). Although this strategy offers financial and administrative efficiencies, it also reveals unresolved regulatory concerns related to transparency, accountability, and the protection of sensitive data. A further and particularly serious concern involves algorithmic bias. Since AI models are trained on pre-existing datasets, they tend to reproduce, and in some cases amplify, structural inequalities embedded in historical records. Within anti-corruption and compliance frameworks, this may result in inaccurate risk assessments or false accusations that disproportionately affect specific ethnic groups, occupational categories, or local communities. Empirical research from the United States demonstrates that facial recognition systems perform markedly worse when identifying individuals with darker skin tones, a deficiency largely attributable to unbalanced training data (OECD, 2022). Such shortcomings become especially problematic when AI technologies are deployed in policing, investigations, or judicial decision-making, where errors can carry severe legal and social consequences. Governing AI for Integrity Artificial intelligence offers powerful new possibilities for combating corruption; however, its effectiveness is inseparable from the quality of the governance frameworks that guide its use. AI-driven anti-corruption measures, whether initiated by state institutions or grassroots actors, must be anchored in openness, clear lines of responsibility, sustained human supervision, and ongoing efforts to detect and correct embedded biases (Parliamentary Assembly, 2024). Both centralized and decentralized implementation models encounter distinct limitations. State-led initiatives may unintentionally deepen existing power imbalances, while civil society-driven efforts frequently face barriers stemming from restricted access to high-quality and open datasets (Ralison Ny Avotra et al., 2021). Nevertheless, when AI-assisted anti-corruption technologies are paired with strong public oversight and digital accountability mechanisms, they have the potential to fundamentally reshape the

E-ISSN: 2181-1024 Volume 7, Issue 1 (2026) TSUL Legal Report 87

identification, monitoring, and prevention of corruption-related risks (Ahmadjonov, 2026). In the final analysis, realizing AI’s benefits requires coordinated action among governments, private-sector stakeholders, and civil society organizations. This collaboration must be supported by rigorous ethical principles, adaptive regulatory structures, and continuous system evaluation. Whereas the misconduct of an individual official may produce limited damage, failures in algorithmic systems can generate widespread and immediate harm (Ahmadjonov, 2025b). Ensuring sound AI governance, therefore, extends beyond technical implementation and represents a critical measure of the capacity of contemporary public institutions to govern responsibly. Conclusion To conclude, AI is becoming mainstream to combat corruption in some developed countries. However, corruption in Central Asia remains deeply entrenched, and conventional reforms have delivered limited and uneven results. In this context, artificial intelligence offers a practical tool that is a real game-changer to strengthen anti-corruption systems by improving transparency, reducing discretionary decision-making, and detecting risks more quickly and accurately, especially in public procurement, service delivery, and local governance. Uzbekistan demonstrates both the scale of the challenge and the potential of AI-based tools to support reform when backed by genuine political commitment and institutional change. Nevertheless, AI is not a cure-all. Without strong legal frameworks, high-quality data, algorithmic transparency, and consistent human oversight, AI systems can replicate bias, widen inequalities, and undermine trust in public institutions. Misuse through political manipulation, intrusive surveillance, or opaque automated decisions may weaken democratic accountability rather than reinforce it. Ultimately, the effectiveness of AI in combating corruption depends less on technological sophistication than on ethical standards, regulatory strength, and inclusive implementation. In the fast-paced world, AI is the most promising remedy for corruption issues facing us. Being able to leverage AI requires cooperation among government, the private sector, and civil society, supported by clear laws, continuous monitoring, and public scrutiny. When properly governed, AI can enhance integrity and accountability; vice versa, it can concentrate and entrench power. Achieving the right balance, therefore, is a test of governance capacity in the digital age.