This conversation explores the necessity of evolving AI governance from simple human checkpoints to comprehensive sociotechnical frameworks. As artificial intelligence operates at speeds and complexities that exceed human cognitive limits, traditional oversight often becomes merely ceremonial. To ensure meaningful human control, organizations must integrate monitoring, documentation, and intervention tools throughout the entire model lifecycle. Failure to implement these robust systems can result in performance degradation, legal liabilities, and the long-term erosion of professional expertise. Ultimately, they advocate for a human-centered approach that treats oversight as a continuous quality assurance process rather than a final approval step.

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