An Interdisciplinary Study of Artificial Intelligence Governance: Engineering Reliability and Legal Accountability

Abstract

The study examined the interdisciplinary aspects of Artificial Intelligence Governance by analyzing the relationship between engineering reliability and legal accountability in autonomous AI systems. The research focused on the role of technical reliability in ensuring safe and trustworthy AI operations, the legal challenges associated with AI-driven decision-making, and the need for integrated governance frameworks combining technical, legal, and ethical perspectives. A qualitative and descriptive research design was adopted using secondary data collected from journal articles, policy reports, books, legal documents, and international AI governance frameworks. Thematic and comparative analysis techniques were applied for data interpretation.The findings revealed that transparency, explainability, fairness, cybersecurity, and robustness were essential for improving AI reliability and public trust. The study also identified major legal accountability challenges, including liability issues, algorithmic bias, privacy concerns, and regulatory gaps. The research concluded that effective AI governance required an interdisciplinary framework integrating engineering standards with legal and ethical regulations to ensure responsible and sustainable AI development.