The governance frameworks for AI in institutional decision-making are being built while AI is being deployed. The gap between the two is where the most consequential harms are accumulating.
The Deployment-Governance Gap
AI systems are being deployed in consequential institutional decision contexts at a pace that continues to outrun the development of the governance frameworks required to ensure their responsible use. The gap is not primarily technical — the technical requirements for responsible AI deployment are reasonably well understood. The gap is institutional: the regulatory frameworks that would require these technical features are incomplete, the organisational governance structures that would implement them internally are underdeveloped, and the accountability mechanisms that would create consequences for their absence are insufficiently enforced. The consequential harms that accumulate in this governance gap include the documented pattern of AI systems that perform significantly worse for specific demographic groups, deployed at scale in high-stakes decision contexts without the bias testing that would have identified the differential performance before deployment.
Building the Governance While Deploying
Building governance while deployment is underway requires the governance minimum viable product: the specific governance features — accuracy validation, bias assessment, explainability for adverse decisions, meaningful oversight — that are non-negotiable for deployment in specific high-stakes contexts, implemented before those deployments proceed, with the understanding that governance maturity will develop incrementally as deployment experience accumulates.
AI governance built after deployment is governance that has already paid the cost of its absence. The institutions that build governance alongside deployment pay a fraction of the cost — in development time, in remediation of identified problems before they scale, and in the legitimacy that proactive governance creates.
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