Findings indicate that algorithmic explainability serves as a prerequisite for legal liability determination, while clear legal regulations provide institutional safeguards for technological transparency.
Abstract
The deep integration of artificial intelligence (AI) in auditing has significantly enhanced audit efficiency and data processing capabilities. However, the inherent “black box” nature of algorithms poses severe challenges in determining liability for audit failures, which hinders further development of AI auditing technologies. This study aims to address liability attribution dilemmas caused by algorithmic opacity in AI auditing contexts. By integrating algorithm governance theory with extended audit accountability theory, we establish a bidirectional framework of “algorithmic explainability and legal liability.” The research first analyzes the real-world contradiction between explainability deficiencies and legal lag in current AI auditing practices, revealing Deloitte's liability determination challenges stemming from generative AI hallucinations. Solutions are then proposed from both technical and legal dimensions. Findings indicate that algorithmic explainability serves as a prerequisite for legal liability determination, while clear legal regulations provide institutional safeguards for technological transparency. This framework not only clarifies accountability chains in human-machine collaboration but also offers theoretical support and practical guidance for promoting compliance in AI auditing and maintaining capital market trust mechanisms.
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
The wide implementation of advanced Machine Learning (ML) models in digital payment systems, especially for fraud detection and credit risk assessment, has substantially improved operational efficiency and transaction security. The inherent opacity, often referred to as the black box character, of these high-performing algorithms poses considerable and mounting issues related to algorithmic fairness, stakeholder trust, and compliance with regulations. This article analyzes the growing strategic significance of Explainable Artificial Intelligence (XAI) as an important governance tool for mitigating algorithmic risk in financial services. The paper exposes how XAI, informed by Agency Theory and Institutional Theory, is not just a technical requirement but an essential institutional mechanism for ensuring regulatory accountability within frameworks like the EU AI Act, restoring public trust and identifying and alleviating systemic algorithmic bias in credit scoring and fraud risk assessment. A conceptual framework is introduced and it illustrates how XAI; using post-hoc interpretation methods such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations)- bridges the knowledge disparity between intricate AI models and various human stakeholders, including customers, fraud analysts, and regulators. This transformation shifts AI from a hypothetical institutional liability to a responsible, auditable, and governable asset within the digital payment ecosystem. The report concluded by describing key areas for forthcoming empirical research on the organizational problems associated with XAI implementation across various regulatory jurisdictions
Temitope Onibaniyi, Umar Lawal· Texila international journal...· 0 citations
The integration of artificial intelligence (AI) into auditing has created a paradigm shift, presenting both unprecedented opportunities to enhance audit quality and significant policy challenges that threaten the foundations of professional judgment. This systematic literature review analyses peer-reviewed articles to synthesize the current landscape of AI in auditing and identify the primary policy challenges confronting the profession. Our analysis reveals a fundamental tension between the automation of audit tasks and the preservation of professional skepticism and judgment. Key themes emerging from the literature include the paradox of professional judgment in an automated environment, the double-edged sword of AI in enhancing audit quality while introducing new risks, the critical need for transparency and explainability in AI systems, the pervasive threat of algorithmic bias, and the significant gaps in regulatory frameworks and professional standards. The findings indicate that while AI offers powerful tools for data analysis, fraud detection, and risk assessment, its adoption is hampered by a complex web of ethical, technical, and organizational barriers. The primary policy challenges identified include regulatory lag, the erosion of professional identity, new quality assurance demands, evolving competency standards, the need for robust ethical frameworks, unresolved liability issues, and a lack of standardization. This review concludes that the auditing profession is at a critical juncture, requiring a concerted effort from regulators, standard-setters, firms, and educators to navigate the transformative impact of AI. I propose a research agenda focused on the long-term effects of AI on professional judgment, the effectiveness of governance models, and the development of new audit methodologies that effectively integrate human and machine intelligence.
Geoffrey Odoch· International journal of com...· 0 citations
Artificial intelligence has become a critical enabling technology for intelligent decision-support systems, creating new opportunities and challenges for trustworthy information processing in digital governance environments. This study investigates the fairness risks and regulatory mechanisms of AI-assisted judicial decision-making by establishing an integrated framework that combines algorithm interpretability, data governance, bias mitigation, and human–machine collaborative control. The proposed framework systematically analyzes the impacts of black-box reasoning, training-data bias, and excessive technological dependence on decision reliability and procedural fairness. To address these challenges, explainable reasoning standards, full-lifecycle data governance strategies, algorithmic fairness auditing, and human-supervised decision protocols are incorporated into a unified regulatory architecture. Furthermore, a traceable reasoning mechanism and adaptive oversight framework are introduced to improve transparency, accountability, and operational robustness in AI-assisted decision systems. The proposed methodology provides practical guidance for explainable intelligent systems, trustworthy information processing, adaptive decision support, and distributed human–AI collaboration, offering potential references for intelligent sensing, secure information management, and next-generation digital service infrastructures.
— This study investigates how Artificial Intelligence (AI) can transform auditing by improving fraud detection and audit quality. However, traditional methods, limited by sampling and manual inspection, failed to detect complex frauds such as Luckin Coffee. Using the Technology – Organization – Environment (TOE) framework and Socio-Technical Systems (STS) theory, the research adopted a qualitative case study design. Audit failures were reconstructed through triangulated evidence and counterfactual reasoning, assessing how Machine Learning (ML) and Natural Language Processing (NLP) could have detected anomalies in real time. Findings show AI provides broader coverage, timeliness, and granularity while preserving professional skepticism. The study contributes by situating AI adoption within socio-technical and organizational contexts and proposing a framework for responsible implementation, and it is crucial to position AI as a tool that complements, rather than substitutes, human auditors.
Hong Yang· International Journal of Tra...· 0 citations
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.
Eldin Rahardian, M. Syahputra, A. Info· Moccasin Journal De Public P...· 0 citations