Published February 3, 2026 | Version v1
Preprint Open

From Theatrical Safety to Mathematical Certainty

Contributors

Researcher:

Description

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-cross safety floors
• requires cryptographically signed human authorization (Restoration Signature Index, Sᵣ) for any system recovery

This architecture enables:

• sub-second intervention
• non-bypassable enforcement
• zero-trust governance boundaries
• auditable, human-sovereign control of autonomous behavior

The AIS Protocol provides a mathematically enforceable alternative to checklist-based safety and is intended for runtime governance of agentic systems operating under real-world latency constraints.

Files

From Theatrical Safety to Mathematical Certainty.pdf

Files (265.3 kB)

Additional details

Additional titles

Alternative title (English)
The Pearson AIS Protocol v1.0

Related works

Describes
Dataset: https://huggingface.co/datasets/JuniperPearson/AIS-Governance-Architecture (URL)
Documents
Publication: 10.5281/zenodo.18437197 (DOI)
Publication: https://zenodo.org/records/18436512 (URL)
Is published in
Journal: www.linkedin.com/in/juniperpearson (Other)

Dates

Other
2026-02-03