The Pearson AIS Protocol v1.0: From Theatrical Safety to Mathematical Certainty
This paper introduces the Pearson AIS Protocol (Alignment Integrity Systems), a control-systems framework for deterministic governance of autonomous and agentic AI systems.
Current AI safety approaches rely primarily on policies, audits, and post-hoc review mechanisms operating at human timescales. As autonomous systems act at machine speed, these methods introduce a governance latency gap in which violations occur before intervention is possible.
AIS reframes ethics enforcement as a real-time control problem rather than a procedural or linguistic one.
The protocol:
• models ethical intent as a versioned state-space constraint
• continuously measures runtime behavior against a certified ethical ground truth (Eₖ)
• enforces alignment geometrically using an Ethical Coherence metric (cosine similarity)
• applies proportional friction (L_AIS) to slow only drifting dimensions
• establishes deterministic no-…
This publication builds directly on the prior AIS governance architecture presented in AIS Governance Architecture (Zenodo record: https://zenodo.org/record/18436512 ), extending that framework with a formal governance metric and refined control logic for evaluating alignment stability over time. Together, these works define a coherent, staged approach to machine-time governance: first establishing the architectural conditions for human authority, and then introducing a measurable indicator for assessing whether those conditions are being preserved as systems evolve.
The present work is intended to support AI governance research, institutional oversight design, and emerging technical standards discussions by providing a structured, continuity-preserving model for human-governed AI. It does not describe a deployed system or release implementation details.
This paper introduces Layer C, the restoration and certification component of the Pearson Alignment Integrity Systems (AIS) Protocol. While most AI governance frameworks focus on detection, alignment, and containment, few define a formal mechanism for safe resumption after an autonomous system has been halted.
Layer C reframes recovery as a deterministic, measurable control process rather than a discretionary operational judgment. Autonomous execution may resume only when three independent conditions are simultaneously satisfied:
State Integrity: verified recovery toward a certified ethical reference state,
Temporal Integrity: bounded response latency that preserves safety under time constraints, and
Authority Integrity: explicit, cryptographically verifiable human authorization.
These dimensions are treated as non-fungible constraints and are composed multiplicatively, ensuring that failure in any single axis collapses the final integrity score and prevents system resumption.
By fo…
Upload From Theatrical Safety to Mathematical Certainty.pdf
This paper introduces the Pearson AIS Protocol (Alignment Integrity Systems), a control-systems framework for deterministic governance of autonomous and agentic AI systems.
Current AI safety approaches rely primarily on policies, audits, and post-hoc review mechanisms operating at human timescales. As autonomous systems act at machine speed, these methods introduce a governance latency gap in which violations occur before intervention is possible.
AIS reframes ethics enforcement as a real-time control problem rather than a procedural or linguistic one.
The protocol:
• models ethical intent as a versioned state-space constraint
• continuously measures runtime behavior against a certified ethical ground truth (Eₖ)
• enforces alignment geometrically using an Ethical Coherence metric (cosine similarity)
• applies proportional friction (L_AIS) to slow only drifting dimensions
• establishes deterministic no-cross safety floors
• requires cryptographically signed human authorization (Rest…
Upload Introduction: The Pearson AIS Protocol v1.0.pdf
This publication builds directly on the prior AIS governance architecture presented in AIS Governance Architecture (Zenodo record: https://zenodo.org/record/18436512 ), extending that framework with a formal governance metric and refined control logic for evaluating alignment stability over time. Together, these works define a coherent, staged approach to machine-time governance: first establishing the architectural conditions for human authority, and then introducing a measurable indicator for assessing whether those conditions are being preserved as systems evolve.
The present work is intended to support AI governance research, institutional oversight design, and emerging technical standards discussions by providing a structured, continuity-preserving model for human-governed AI. It does not describe a deployed system or release implementation details.
## Overview The Pearson Alignment Integrity Systems (AIS) Protocol is a deterministic governance framework for autonomous and agentic AI systems. This dataset contains formal specifications, mathematical definitions, and reference material describing **Layer C: Deterministic Restoration Integrity**, which enforces: - Real-time alignment verification during execution - Safety-dominant restoration metrics - Latency-sensitive human-in-the-loop authorization - Cryptographically bound restoration events The protocol treats AI governance as a **control-layer problem**, not a post-hoc audit exercise. ## Contents - Formal definitions of alignment and restoration integrity - Safety-dominant SRA (Sovereignty–Risk–Auditability) model - Latency-weighted restoration performance metrics - State-bound cryptographic authorization logic - Reference implementations and pseudocode (where applicable) ## Intended Use This dataset is intended for: - AI safety research - Governance and alignment system design - Safety-critical autonomous systems - Human-in-the-loop control architectures It is **not** a training dataset and is not designed for direct model optimization. ## License Released under Creative Commons Attribution 4.0 (CC BY 4.0).
This paper introduces Layer C, the restoration and certification component of the Pearson Alignment Integrity Systems (AIS) Protocol. While most AI governance frameworks focus on detection, alignment, and containment, few define a formal mechanism for safe resumption after an autonomous system has been halted.
Layer C reframes recovery as a deterministic, measurable control process rather than a discretionary operational judgment. Autonomous execution may resume only when three independent conditions are simultaneously satisfied:
State Integrity: verified recovery toward a certified ethical reference state,
Temporal Integrity: bounded response latency that preserves safety under time constraints, and
Authority Integrity: explicit, cryptographically verifiable human authorization.
These dimensions are treated as non-fungible constraints and are composed multiplicatively, ensuring that failure in any single axis collapses the final integrity score and prevents system resumption.
By fo…
