care-reference

The CARE governance model for enterprise AI separates human trust decisions from AI execution, using the Dual Plane Model to preserve human accountability while allowing autonomous operation. It clarifies accountability boundaries, trust chains, and transparency for policy-aligned AI systems.

The Human-on-the-Loop operating model enables AI autonomous execution while keeping humans directed in trust and responsibility. It avoids human-in-the-loop bottlenecks by defining accountability boundaries and supporting graceful degradation when interventions are required.

The Dual Plane Model distinguishes human trust decisions from AI execution planes, while the Mirror Thesis reflects AI operations back to human oversight. Together they define accountability boundaries and trust chains that keep autonomous systems human-directed.

CARE relates to EATP, COC, and Kailash as complementary frameworks for enterprise-scale coordination. It provides the governance layer for accountability and trust boundaries, while the others handle broader operational and coordination tasks across the organization.

You set accountability boundaries by separating human trust decisions from AI execution using CARE governance. Define trust chains, transparency requirements, and graceful degradation policies to ensure operations remain policy-aligned and human-directed.

Use CARE governance when human-in-the-loop workflows create execution bottlenecks. CARE's Human-on-the-Loop model allows autonomous AI operation while preserving human accountability through trust chains and defined intervention boundaries.