arXiv:2606.15563v1 Announce Type: new Abstract: AI systems increasingly delegate decisions to specialized models, evaluators, tools, and supervisory controllers. The central AI problem is no longer only model accuracy, but uncertainty-aware governance: how much autonomy to grant, which evidence should calibrate trust, what performance ceiling a delegated AI system can sustain, and when human intervention becomes necessary. We propose the Minimum Sufficient Oversight Principle (MSO), a variational principle for principled autonomy delegation: minimize governance burden on the Fisher information
As AI systems become more complex and autonomous, the challenge of managing their decision-making and ensuring reliability without constant human oversight becomes critical for their widespread adoption and trust.
This research addresses a foundational challenge for deploying advanced AI: how to balance autonomy with safety and control, establishing principles for scalable and trustworthy AI governance beyond mere model accuracy.
The focus shifts from solely optimizing AI model accuracy to developing robust uncertainty-aware governance mechanisms, enabling AI systems to operate with minimal human intervention while maintaining safety thresholds.
- · AI developers
- · High-autonomy AI systems
- · Regulators and policymakers
- · Industries adopting AI
- · AI systems lacking robust governance
- · Opaquely developed AI solutions
- · Human review-intensive AI workflows
The MSO principle provides a framework for designing AI systems with inherent and measurable levels of autonomy and oversight.
This could lead to standardized methodologies for auditing and certifying AI system safety and reliability, especially in critical applications.
Accelerated deployment of highly autonomous AI agents in sensitive domains, with regulatory bodies requiring adherence to principles like MSO.
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Read at arXiv cs.AI