Policy and the missing links in AI governance

Policy gaps and governance models for artificial intelligence require careful attention from stakeholders across the regulatory landscape, as demonstrated by comparative analysis of existing frameworks.

Key context and overview; The article examines how voluntary governance frameworks in Corporate Social Responsibility (CSR) and AI domains can complement each other to build more robust AI governance systems.

• A comparison between ISO 26000 (CSR framework) and NIST AI Risk Management Framework reveals critical gaps in current AI governance approaches
• Unlike CSR frameworks, AI governance lacks standardized reporting mechanisms and metrics for risk assessment
• Framework effectiveness depends heavily on ecosystem integration rather than isolated implementation

ISO 26000 as a model framework; The CSR standard demonstrates how voluntary frameworks can succeed when supported by complementary mechanisms and reporting standards.

• Developed with input from 90+ countries and 40 international organizations
• Functions within an ecosystem of interconnected standards and regulatory requirements
• Success stems from flexibility in local adaptation while maintaining global consistency
• Integrates with practical reporting tools like the Global Reporting Initiative (GRI)

Current state of AI governance; The NIST AI Risk Management Framework represents a significant step forward but reveals several ecosystem weaknesses.

• Framework provides systematic guidance through four functions: Govern, Map, Measure, and Manage
• Lacks standardized reporting mechanisms comparable to GRI in the CSR domain
• Organizations struggle with communication barriers between policy and technical teams
• Rapid AI advancement often outpaces governance mechanisms

Key recommendations; Drawing from CSR experience, several priorities emerge for strengthening AI governance.

• Develop comprehensive metrics for measuring algorithmic bias, model explainability, and system accountability
• Create verification infrastructure combining technical expertise with practical implementation experience
• Establish cross-border reporting standards that work across jurisdictions
• Implement continuous monitoring systems rather than annual reporting cycles

Looking ahead: Bridging implementation gaps; Success in AI governance will require careful attention to ecosystem development and standards integration.

• Research must evaluate how voluntary frameworks interact with regulations
• Metrics development demands balance between technical precision and practical applicability
• Global coordination remains essential for effective implementation
• Stakeholder engagement across technical and policy domains will be crucial for success