Investigating Algorithm Review Boards for Organizational Responsible Artificial Intelligence Governance

Algorithm Review Boards (ARBs) are emerging as a critical governance tool across sectors: The study found that ARBs and similar review committees are among the most impactful internal responsible AI governance approaches organizations are implementing, with particularly extensive use in health and finance sectors.

One-size-fits-all approaches are insufficient: Organizations define "responsible AI" differently and employ varied governance strategies. Importantly, the research contradicts the assumption that existing Institutional Review Boards alone are adequate for overseeing AI systems, indicating that specialized algorithm review processes are necessary.

Leadership support and integration with existing systems are key success factors: The study identified integration with existing internal regulatory approaches and buy-in from organizational leadership as among the most important attributes for effective RAI governance, suggesting that AI oversight works best when embedded into established institutional structures rather than siloed.

Financial constraints pose the greatest practical challenge: Despite recognizing the importance of responsible AI governance, organizations face significant financial tensions that impede effective implementation of RAI measures, indicating a gap between commitment and resource allocation.

Governance approaches vary meaningfully by sector and organization type: The research revealed robust model governance practices in finance sectors and extensive algorithm governance in health sectors, suggesting that different industries face distinct AI risks and have developed sector-specific governance responses.