Multi-Stakeholder AI Governance Dashboard: Bridging Technical Complexity and Business Accessibility
DOI:
https://doi.org/10.47941/ijce.2984Keywords:
AI Governance Democratization, Multi-Stakeholder Transparency, User-Centered Governance Interfaces, Cross-Functional AI Accountability, Ethical AI Scaling FrameworksAbstract
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.
Downloads
References
World Bank Group, "Digital Progress and Trends Report 2023," 2023. [Online]. Available: https://www.worldbank.org/en/publication/digital-progress-and-trends-report
Sunil Zarikar, "Building a Scalable Data Governance Framework for AI and ML: A Beginner’s Guide," International Journal of Computer Trends and Technology, vol. 73, no. 5, pp. 12-18, May 2025. [Online]. Available: https://www.linkedin.com/pulse/building-scalable-data-governance-framework-ai-ml-guide-sunil-zarikar-o0xyf/
Nana B. Amonoo-Neizer, "Building Blocks of an Open Source AI Governance Framework," LinkedIn, 2025. [Online]. Available: https://www.linkedin.com/pulse/building-blocks-open-source-ai-governance-framework-amonoo-neizer-fzb0e/
I. Carlos J. Costa et al., "The Democratization of Artificial Intelligence: Theoretical Framework," Applied Sciences, vol. 14, no. 18, p. 8236, Sep. 2024. [Online]. Available: https://www.mdpi.com/2076-3417/14/18/8236
Alex Wall, "User-Centric Design: The Key to Robust AI Governance," LinkedIn, 2025. [Online]. Available:https://www.linkedin.com/pulse/user-centric-design-key-robust-ai-governance-alex-wall-3el4c/
CoreSite, "Democratizing AI: Benefits, Challenges and Governance," 2024. [Online]. Available: https://www.coresite.com/blog/democratizing-ai-benefits-challenges-and-governance
Juliana George, "Distributed Accountability: Designing Governance Architectures for Multi-Agent AI Ecosystems,"ResearchGate,2024.[Online].Available:https://www.researchgate.net/publication/391015025_DISTRIBUTED_ACCOUNTABILITY_DESIGNING_GOVERNANCE_ARCHITECTURES_FOR_MULTI-AGENT_AI_ECOSYSTEMS
Narayana Pappu, "AI Metrics 101: Measuring the Effectiveness of Your AI Governance Program," ZenData Blog, 2024. [Online]. Available: https://www.zendata.dev/post/ai-metrics-101-measuring-the-effectiveness-of-your-ai-governance-program
Consilien, "AI Governance Frameworks: Guide to Ethical AI Implementation," 2025. [Online]. Available: https://consilien.com/news/ai-governance-frameworks-guide-to-ethical-ai-implementation
Alice Gomstyn and Alexandra Jonker, "Democratizing AI: What does it mean and how does it work?" IBM Think Insights, 2024. [Online]. Available: https://www.ibm.com/think/insights/democratizing-ai
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Guru Bhaskar Reddy Duggireddy

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.