This work introduces an explained, risk-conscious, and secure-by-design autonomic architecture of large scale distributed computing systems defining dynamic cloud-edge environments. The main issue it is expected to fix is the shortage of transparency, uncertainty quantification, and combined cybersecurity of the traditional black-box AI-driven distributed systems that restricts trust, scalability, and resiliency. In order to address these limitations, the suggested methodology combines some structured data preprocessing (Z-score filtering, entropy and variance extraction), probabilistic risk modeling, explainable AI based on SHAP, and Kubernetes KEIO-enabled adaptive orchestration into a single layered framework. Experimental analysis on cloud anomaly data sets shows good predictive accuracy of 91.1 percent, recall of 86 percent as well as precision of 79 percent as represented in the confusion matrix of 3333 true negative, 1000 true positive, 261 false positive, and 160 false negative. Risk scores are centred on 0.0, with most of the risk scores falling within the range of - 0.3 to 0.3, which is stable with respect to calibration. System availability indicates 94% of uptime, Mean Time to Recovery (MTTR) of 3.45 and 48% of scalability efficiency, which all indicate high uptime and efficient fault management. SHAP analysis reveals that power consumption (0.28) and memory usage (0.27) had the largest contributions to risks. In general, the architecture is able to implement transparent, resilient and scalable autonomous decision-making which is appropriate to mission critical distributed infrastructures.
Abdinasir Ismael Hashi, Osman Abdullahi Jama, I. Abdullahi· ITEGAM- Journal of Engineeri...· 0 citations
Corporate boards now sit atop decision architectures that no longer belong to them alone. Machine learning systems screen credit applications, price insurance risk, recommend mergers, flag fraud, and increasingly shape the strategic judgments that directors and executives once reached through experience and deliberation. When one of these systems produces a harmful, discriminatory, or commercially damaging outcome, existing governance doctrine struggles to identify who answers for it. This paper examines the widening gap between algorithmic decision-making and the accountability structures built for human agents. Drawing on agency, stakeholder, stewardship, institutional, and resource dependence theories, together with enterprise risk management and responsible AI scholarship, the paper argues that accountability for algorithmic harm cannot rest on a single actor or a single governance layer. Responsibility instead needs to be distributed across the people and functions that design, approve, deploy, and supervise an AI system, with each layer answerable for a distinct category of failure: design flaws, oversight lapses, deployment misjudgment, and monitoring neglect. The paper's central contribution is a multi-level Corporate AI Accountability Governance Framework that assigns differentiated responsibilities to the board, executive leadership, a dedicated AI governance committee, risk management, internal audit, technology teams, external vendors, and regulators. The framework is built around a feature that conventional governance controls were never designed to handle: AI systems continue to change after deployment, so a one-time approval cannot substitute for ongoing supervision. The paper closes with practical implications for boards preparing for algorithmic oversight, a comparative reading of regulatory expectations across the European Union, the United States, the United Kingdom, and selected Asia-Pacific economies, and a research agenda for scholars working on the next phase of digital corporate governance.
I. Abdullahi· Scholedge International Jour...· 0 citations