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We are developing a labor-centered governance architecture for institutions integrating AI agents at scale. This framework translates ethical commitments into enforceable institutional design so that authority, accountability, and decision integrity are structurally embedded, not just aspirational.

Why This Matters

Traditional governance systems were built for human-only decision environments. As AI accelerates workflows and decision velocity, institutional risk isn’t just technical — it is institutional: opaque authority, unreviewable reasoning paths, assumption-stacking, and unaccountable risk allocation. Our framework is designed to make these dynamics visible, reviewable, and controllable before they become crises.

Core Components of the Framework

1. Authority: Defines who decides and under what constraints. Institutional power without clear bounds fuels incoherence.

2. Responsibility: Specifies who bears the consequences when harm occurs, closing the accountability gap.

3. Consent: Ensures workers — including those interacting with AI systems — have meaningful agreement in decisions that affect them.

4. Contestability: Establishes mechanisms for review and challenge of high-impact decisions, preventing frozen hierarchies and hidden power. (Contestability parallels best practices in modern governance frameworks where stakeholders have structured avenues to raise and resolve disputes.)

5. Documentation: Mandates what must be recorded to preserve transparency and enable audit and historical reasoning traceability. Traceability is a key principle for operational accountability in computable systems.

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