Bridging the Gap: A Five-Layer Approach to AI Governance

In the rapidly evolving landscape of artificial intelligence (AI), ensuring that systems operate ethically and in compliance with regulations is paramount. However, many existing governance frameworks fall short in translating overarching regulatory principles into actionable steps, leading to gaps in compliance and enforcement. Addressing this critical issue, researchers Avinash Agarwal and Manisha J. Nene have proposed a structured five-layer AI governance framework that bridges the divide between high-level mandates and practical implementation. This framework progresses from broad regulatory mandates to specific standards, assessment methodologies, and certification processes, providing a clear pathway for organizations to meet technical, regulatory, and ethical requirements. Its adaptability is demonstrated through case studies on AI fairness and incident reporting, highlighting its effectiveness in identifying and addressing gaps in legal mandates and standardization. By offering a clear and actionable roadmap, this framework equips policymakers, regulators, and industry stakeholders with the tools to enhance compliance and risk management, ultimately fostering public trust and promoting the ethical use of AI for societal benefit.

The five-layer framework begins with broad regulatory mandates that set the foundational principles for AI governance. The second layer involves the development of specific standards that interpret these mandates into more detailed guidelines. The third layer focuses on creating assessment methodologies to evaluate AI systems against these standards. The fourth layer establishes certification processes to formally recognize compliance. Finally, the fifth layer involves continuous monitoring and updating of these processes to adapt to the evolving AI landscape. This structured approach ensures that each layer builds upon the previous one, creating a cohesive and comprehensive governance framework. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting, demonstrating its ability to identify gaps in legal mandates, standardization, and implementation. This adaptability allows the framework to cater to both global and region-specific AI governance needs, effectively mapping regulatory mandates with practical applications to improve compliance and risk management.