Jul 2026· Moccasin Journal De Public Perspective· Vol 3, pp. 128-141· 0 citations· 35 references
TL;DR
It is demonstrated that AI-generated evidence may affect defendants’ ability to challenge evidentiary claims, thereby creating potential risks to procedural fairness and fair trial rights.
Abstract
This study examines the legal implications of artificial intelligence-generated evidence in criminal proceedings, focusing on evidentiary admissibility, due process protection, and regulatory accountability. Using a doctrinal legal research design combined with comparative legal analysis, the study analyzes legislation, judicial developments, policy documents, and academic literature concerning the use of AI-generated evidence across multiple jurisdictions. The findings reveal that existing evidentiary doctrines remain applicable but are increasingly challenged by algorithmic opacity, limited explainability, reliability concerns, and the emergence of synthetic media. The study further demonstrates that AI-generated evidence may affect defendants’ ability to challenge evidentiary claims, thereby creating potential risks to procedural fairness and fair trial rights. The novelty of this research lies in the development of an integrated normative framework that connects traditional evidentiary principles with contemporary AI governance standards. The study contributes to ongoing debates regarding the regulation of AI within criminal justice systems and provides practical recommendations for strengthening transparency, accountability, and procedural safeguards in the use of AI-generated evidence.
The increasing use of artificial intelligence in criminal justice is no longer a distant prospect but an emerging reality that is gradually reshaping procedural practices. Algorithmic tools are increasingly involved in risk assessment, data analysis, and evidentiary evaluation, particularly in cases involving complex digital environments. The main aim of this study is to examine the legal implications of integrating artificial intelligence into criminal proceedings, with a particular focus on its impact on fair trial guarantees and procedural safeguards. The analysis draws on international human rights standards, selected jurisprudence of the European Court of Human Rights, and recent developments in European Union regulatory approaches to artificial intelligence. The research is based on a doctrinal and comparative legal methodology, allowing for an assessment of how existing legal frameworks respond to the challenges posed by algorithmic decision-making. Particular attention is given to issues of transparency, contestability, and the ability of parties to effectively challenge AI-generated outcomes. The findings indicate that the opacity of algorithmic systems creates significant difficulties for maintaining equality of arms, particularly in situations where procedural rules do not adequately ensure meaningful access to the reasoning underlying automated outputs. It is argued that without clearly defined safeguards ensuring transparency, accountability, and proportionality, the use of artificial intelligence risks undermining the fairness and legitimacy of criminal proceedings
K. Khalilov· Development Through Research...· 0 citations
This study aims to examine the construction of criminal liability in cases of medical malpractice involving artificial intelligence and to identify the normative gaps within contemporary health law that hinder effective accountability. It further seeks to formulate a responsive legal framework capable of addressing the challenges posed by the integration of advanced technologies in medical practice. The research employs normative legal methodology using statute and conceptual approaches. The statute approach analyzes existing legal provisions governing healthcare and criminal liability, while the conceptual approach explores doctrinal principles such as fault, negligence, and responsibility in technologically mediated environments. Data are analyzed using a descriptive prescriptive method to both explain current legal conditions and propose normative solutions. The findings reveal that existing legal frameworks remain anthropocentric and are unable to adequately address the distributed nature of responsibility in AI mediated healthcare. The absence of explicit regulations on the use of artificial intelligence creates a normative vacuum, leading to uncertainty in attributing criminal liability among physicians, developers, and healthcare institutions. The study also finds that traditional doctrines such as mens rea and actus reus are increasingly difficult to apply in cases where decision making involves algorithmic systems. As a result, there is a need to reconceptualize criminal responsibility through more adaptive models, including shared responsibility and selective strict liability. This research concludes that legal reform is essential to ensure accountability, legal certainty, and patient protection in the era of digital healthcare. It proposes the development of integrated and forward looking legal frameworks that align technological innovation with fundamental principles of criminal law.
Nirwan Afandy, Hasbuddin Khalid, Satri Hasyim· Golden Ratio of Law and Soci...· 0 citations
This study explores the legal and evidentiary challenges in proving corporate cyber harm caused by artificial intelligence (AI). It underscores the difficulty of attributing responsibility to corporate entities when AI systems operate autonomously. Persistent obstacles arise in the collection, preservation, and authentication of volatile digital evidence across jurisdictions. Furthermore, the paper examines the complexity of establishing causation between AI-driven cyberattacks and the resulting damage, thereby compounding these challenges and undermining traditional evidentiary standards. It emphasizes the urgent need for adaptive legal frameworks, strengthened international cooperation, and improved judicial capacity to handle electronic evidence. Recommendations are proposed to reinforce corporate accountability by clarifying liability models, promoting explainable AI, and encouraging shared responsibility. Finally, the study advocates for a harmonized global governance of cyber harm by integrating civil liability doctrines with digital evidence standards. By bridging legal and technical perspectives, the research contributes to a more coherent framework for prosecuting offenders.
O. Althnaibat, S. Altarawneh, R. Hmaidan et al.· Corporate Law & Governan...· 0 citations
This study aims to analyze the legal implications of the use of generative artificial intelligence (generative AI) in journalistic practice against three forms of press violations, namely defamation, the spread of misinformation/disinformation, and privacy violations, as well as to assess the extent of the readiness of the applicable legal framework in overcoming these problems. The development of generative AI in journalism presents new challenges in criminal law, especially regarding accountability for news content generated automatically without direct human intervention. This study uses a normative-doctrinal approach combined with a comparative analysis of the Indonesian criminal law framework and the latest international regulatory developments. The results of the study show that conventional criminal accountability principles, especially the elements of mens rea and actus reus, are not fully compatible with the characteristics of AI which is autonomous, thus causing legal gaps and regulatory uncertainty. To overcome these problems, this study offers three strategic solutions, namely: (1) modification of the corporate accountability model by expanding the definition of criminal offenders, (2) the application of limited strict liability (limited strict responsibility) for digital platforms, and (3) the establishment of a risk-based algorithmic supervision mechanism involving various stakeholders. This research makes a theoretical contribution to the development of criminal law in the digital era while offering practical recommendations for policymakers in formulating an AI accountability system that is adaptive, balanced, and supports modern journalism innovation.
Zainal Arifin, E. Handayani, Souad Ezzerouali· Kosmik Hukum· 0 citations
From the perspective of digital rule of law, artificial intelligence provides important technical support for judicial modernization by improving judicial efficiency, unifying judgment standards, optimizing litigation services, and strengthening trial management. However, the judicial application of AI also generates risks including algorithmic bias, data-security threats, ambiguous accountability, procedural alienation, weakened judicial subjectivity, and insufficient transparency. This study analyzes the development status, value functions, risk types, and generation mechanisms of AI judicial applications, and proposes an integrated regulatory path combining technical governance, legal regulation, ethical norms, procedural safeguards, and accountability. It further constructs a procedural justice protection mechanism involving algorithmic transparency, procedural participation, judicial experience, equality between prosecution and defense, and rights relief. The study expands procedural justice theory under digital conditions and provides a normative framework for AI-assisted judicial systems. Its engineering relevance lies in the transparent design of algorithmic decision pipelines and secure data-governance architectures.
The rapid deployment of artificial intelligence (AI) systems across the European Union (EU) raises profound legal challenges, particularly regarding liability for damage caused by autonomous or semi-autonomous systems. While AI promises significant economic and societal benefits, its increasing autonomy disrupts traditional legal concepts of fault, causation, and responsibility. This tension is clearly illustrated by recent national debates, such as in Croatia, where proposals have emerged to introduce criminal liability for harm caused by AI systems, resulting in new criminal offence introduced in 2025, despite unresolved questions concerning the identification of the responsible legal subject. At the EU level, policymakers have sought to address these challenges through a comprehensive regulatory framework, most notably the Artificial Intelligence Act (AI Act) and the proposed reforms of civil liability rules for defective products and emerging digital technologies. These instruments aim to balance innovation with legal certainty, risk management, and the protection of fundamental rights. However, they predominantly focus on ex ante risk regulation and civil liability mechanisms, leaving unresolved issues regarding criminal liability and the attribution of responsibility in cases involving complex AI-driven decision-making processes. This paper examines the evolving EU approach to liability for AI-caused damage and assesses its implications for legal certainty within member states. The research focuses on three core questions: (1) how EU law conceptualizes responsibility and accountability for AI-related harm; (2) how national legal systems, using Croatia as a case study, attempt to transpose or supplement EU norms through criminal and civil law instruments; and (3) whether existing regulatory models adequately address the practical and normative challenges posed by autonomous AI systems. Methodologically, the paper employs doctrinal legal analysis of EU legislation and policy documents, comparative examination of national legal approaches, and critical review of relevant academic literature. Where available, emerging judicial reasoning and analogies from existing case law on technological risk and product liability are also considered. The paper argues that current regulatory developments reveal a fragmented approach to AI liability that risks undermining legal certainty and uniform application of EU law. It concludes by proposing normative guidelines for a more coherent EU-wide framework that clarifies the attribution of responsibility, enhances harmonization across member states, and strengthens the EU’s role as a global standard-setter in AI governance.
Vedran Kruljac, Suncana Roksandic, Doris Skaramuca· EU and Comparative Law Issue...· 0 citations
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