Algorithmic Governance for Explainability: A Comparative Overview of Progress and Trends

Explainability has become a governance issue, not just a technical one. The paper emphasizes that AI explainability is no longer solely the domain of computer scientists—it now requires coordination across government, business, and international bodies to address the broader societal implications of opaque AI systems.

Lack of explainable AI affects people across all economic and geographic boundaries. The research highlights that the harms from unexplainable AI systems are not limited to wealthy nations or privileged groups, underscoring the urgent need for equitable solutions that protect vulnerable populations globally.

Current governance efforts remain fragmented and incomplete. Despite multiple stakeholders—including public sector agencies, enterprises, and international organizations—working on XAI solutions, the paper concludes that "XAI is still in its infancy," suggesting that existing regulatory frameworks and technical standards are insufficient.

Collaboration between all parties is essential for future progress. The paper stresses that solving the explainability challenge requires ongoing "collaborative engagement" among government, industry, and international organizations, indicating that no single sector can address this problem alone.

Regulatory instruments must evolve alongside AI applications. The research notes that future regulations will need to be developed in tandem with emerging AI applications, suggesting that policymakers cannot simply apply today's rules to tomorrow's technologies.