Deterministic Human Governance for Autonomous Systems The Pearson Alignment Integrity Systems (AIS) Protocol is a public architecture summary describing deterministic human governance for autonomous systems operating at machine-time scales. As AI agents and autonomous control systems transition from advisory tools to agentic operators, traditional oversight fails due to a governance latency gap: unsafe actions can complete before humans can detect, interpret, or intervene. AIS treats this as a control-systems failure and replaces trust-based safety with structural enforcement. AIS introduces a recursive governance loop: D ⇄ A ⇄ B ⇄ C ⇄ D that continuously monitors drift, applies proportional stabilization, triggers non-negotiable containment on boundary violation, and enforces cryptographically verifiable human authorization before any restart. Alignment is measured using Ethical Coherence (ECG) as a state-space coherence signal. Once containment occurs, the system enters Logical Zero, where execution and actuation are suspended. Resumption is governed by a multiplicative restoration integrity condition requiring human authority, certified state integrity, and temporal validity- so restoration cannot be inferred, optimized, or bypassed. AIS also defines a Minimum Viable Secondary Observer (MVSO): a physically isolated, low-complexity root of trust that reads unspoofable telemetry via unidirectional data flow, enforces containment through a hardware halt line, generates a state-bound restoration challenge, and verifies human signatures before releasing actuator power. A worked scenario (ion propulsion thermal runaway under deep-space latency) demonstrates why self-healing autonomy is unsafe without deterministic restoration gates. This release intentionally omits cryptographic keys, thresholds, and hardware specifications. It is intended as a public architecture summary describing governance structure and enforcement logic.
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Juniper Pearson
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Juniper Pearson (Mon,) studied this question.
www.synapsesocial.com/papers/698c1bff267fb587c655e207 — DOI: https://doi.org/10.5281/zenodo.18569792
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