Jul 2026· AI and Ethics· Vol 6· 0 citations· 35 references
Computer Science
TL;DR
It is argued that AI adoption should be understood not merely as automation but as a technology-transfer problem, and the AITTF is introduced, a seven-stage governance model designed to help organisations transfer workflows, expertise and decision-making into AI-enabled systems while maintaining accountability, operational integrity and organisational memory.
The proposed framework offers a real-world action plan for sustainable AI transformation and a theoretical understanding of the phenomenon of AI transformation by offering a structured, risk-mitigated pathway toward high-maturity, AI-enabled enterprise operations.
M. Abuhaimed· Journal of Intelligent Decis...· 0 citations
This paper argues for a transition from AI Governance as Compliance to AI Governance Engineering , a systems-oriented discipline in which governance is embedded throughout the enterprise intelligence lifecycle, enabling enterprise intelligence systems that are secure, explainable, trustworthy, and governable by design.
Faruk Çelikkanat· International Journal of Res...· 0 citations
This paper explores how AI risk governance can be effectively integrated into the epistemological and structural foundations of such organizations through the lens of fourth-order cybernetics, and offers a conceptual pathway for resilient and ethically aligned AI implementation in complex organizational environments.
Ludmila Jiříčková, Petr Doucek· International Scientific Con...· 0 citations
This article examines three interconnected dimensions of responsible AI for enterprise modernization: governance infrastructure for accountable AI deployment, algorithmic equity in high-impact decision environments, and the evolving international regulatory landscape shaping enterprise AI governance.
M. Modi· International Journal of Eng...· 0 citations
The application of artificial intelligence (AI) technologies in the public sector has led to improved public services, enhanced administrative performance, and strengthened automated decision-making. However, the increasing reliance on AI systems has raised concerns regarding accountability, ethical compliance, privacy protection, transparency, human oversight, and risk management. This study, employing both conceptual and qualitative research methodologies, examines the governance factors and requirements for responsible AI implementation in the public sector. The research methodology includes a comparative analysis of international AI governance frameworks and regulations. The study identifies key dimensions influencing responsible AI implementation, such as accountability, human oversight, ethical governance, legal compliance, risk management, and transparency. The findings demonstrate that the adoption of responsible AI cannot be achieved through technological means alone but also requires a commitment to comprehensive governance mechanisms. Furthermore, the sequential interaction and interdependence of governance factors reduce operational and societal risks, increase transparency and explainability, and foster public trust in the systems. This study contributes to enriching the culture and knowledge of AI governance, and the proposed framework helps government sector leaders develop responsible AI governance in accordance with international standards and regulations.
Ghazwan Hani Hussein, Faiza Mohamed, A. Abuzreda· Journal of Technology and Sy...· 0 citations
A governance-oriented framework that links 13 audit-specific prompt types to the main phases of the internal audit lifecycle: planning, fieldwork, reporting, and follow-up is developed and indicates that structured prompting can improve repeatability, transparency, and professional skepticism.
Özden Şentürk· Denetişim· 0 citations
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