Governing Actions, Not Agents: Institutional Attestation as a Governance Model for Autonomous AI Systems
Quick Answer
This paper proposes an institutional governance model for autonomous AI systems, where agents retain autonomy but require independent attestation for high-risk actions.
Quick Take
This paper proposes an institutional governance model for autonomous AI systems, where agents retain autonomy but require independent attestation for high-risk actions. The model ensures accountability through cryptographic verification and tamper-evident logs, demonstrated in clinical prescribing and software deployment scenarios.
Key Points
- Agents maintain autonomy in planning but lack execution authority for high-risk actions.
- Execution of actions requires independent attestation from authoritative sources.
- Decisions are recorded in a tamper-evident log for independent verification.
- The model is illustrated with examples from clinical prescribing and software deployment.
- A proof-of-concept implementation validates the governance model's effectiveness.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2606. 26298v1 Announce Type: new Abstract: Autonomous AI agents may begin to perform consequential, irreversible actions such as clinical prescribing and production software deployment. This paper observes that human institutions have governed powerful autonomous actors not by monitoring their reasoning but by requiring independently attested evidence at the point of consequential action. We formalise this institutional pattern as a computational governance model for AI agent systems.
Under the proposed model, an agent retains full autonomy over planning and reasoning but holds no execution authority over designated high-risk actions. Execution is conditional on preconditions that are each independently attested by a separate authoritative source, cryptographically bound to a declared intent, and evaluated by a deterministic policy. Decisions are recorded in a tamper-evident log amenable to independent re-verification.
We present a proof-of-concept implementation and illustrate the model with examples from software deployment and clinical prescribing.
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