Multi-Stakeholder AI Governance Dashboard: Bridging Technical Complexity and Business Accessibility

Guru Bhaskar Reddy Duggireddy ()

International Journal of Computing and Engineering, 2025, vol. 7, issue 12, 38 - 47

Abstract: This article explores the critical need for standardizing AI governance by transitioning from specialist-centric approaches to inclusive frameworks that engage different stakeholders across associations. As AI systems increasingly impact business-critical decisions and nonsupervisory pressures consolidate encyclopedically, traditional governance models confined to specialized brigades have proven insufficient for managing pitfalls and maintaining trust. The composition presents a comprehensive frame for enforcing accessible AI governance through four foundational rudiments: transparent metadata factors, stakeholder-specific interfaces, cross-functional responsibility structures, and scalable oversight mechanisms. By examining design principles for user-centered governance tools and implementation strategies for distributed accountability, the article demonstrates how organizations can bridge the gap between technical complexity and business accessibility. The article reveals that successful democratization of AI governance depends on transparency as the key enabler, supported by intuitive visualization techniques, role-based access models, and systematic governance literacy programs. Through case studies and emerging stylish practices, the composition illustrates how associations enforcing inclusive governance frameworks witness smaller AI-related incidents and advanced stakeholder trust scores. The unborn vision encompasses tone-governing AI systems, interoperable governance platforms, and public-facing translucency doors that produce a new paradigm of participatory AI oversight, situating associations to thrive in a decreasingly AI-driven business geography while meeting evolving nonsupervisory conditions and societal prospects.

Keywords: AI Governance Democratization; Multi-Stakeholder Transparency; User-Centered Governance Interfaces; Cross-Functional AI Accountability; Ethical AI Scaling Frameworks (search for similar items in EconPapers)
Date: 2025
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