PUBLIC RELEASE — Cabinet Grade — Governance Constrained

GOVERNANCE FRAMEWORK SPECIFICATION — NOT AN OPERATIONAL SYSTEM

MAEGM v1.6 — Global Standards Edition

Public Technical White Paper

Version 1.6 — Public Release Edition

March 9, 2026 | BWR Group Canada — MyBiz AI Division

Governing Framework: MAEGM v1.5 — Seven-Layer Human-First Architecture

CEO & Chief Architect: Brent Richardson

Standards: Condorcet Jury Theorem (1785); Mersenne Scaling (1644); Lamport BFT (1982); EU AI Act; NIST AI RMF; ISO 42001; OECD; UNESCO

DISCLAIMER: This is a governance framework specification describing architectural design principles and regulatory alignment. It is not legal advice. Implementers should consult qualified legal counsel. Open-source governance components are designated for future release under Apache 2.0 (licensing in preparation). Commercial platform components remain proprietary to BWR Group Canada. Cross-platform validation represents independent structural review, not institutional endorsement.

1. What Is MAEGM?

MAEGM (MyBiz AI Ethical Governance Model) is a seven-layer, mathematically verifiable AI governance architecture. It is grounded in the Condorcet Jury Theorem (1785), Mersenne scaling sequences (1644), and Lamport’s Byzantine Fault Tolerance (1982).

The architecture ensures that artificial intelligence systems operate as advisory tools under human authority — never as autonomous decision-makers. Every AI function within the architecture is constrained to recommend, analyze, flag, and inform. No AI function may enforce, escalate, authorize payments, modify policy, or override human decisions.

To our knowledge, MAEGM is the first governance architecture to derive its structural parameters from mathematical proof rather than policy convention. The seven-layer count is not arbitrary — it is the minimum odd number that simultaneously satisfies decisiveness (Condorcet), fault tolerance (Lamport), and scalable governance (Mersenne). The derivation is reproducible. Any mathematician, any computer scientist, any AI platform can run the equations and arrive at the same answer.

2. The Seven Layers

Layer	Function	Core Principle
L7 — Human Governance	Democratic apex	Cabinet-level authority. Human override on every AI function. Absolute halt power. No AI operates above this layer.
L6 — Transparency	Public accountability	All AI interactions logged. Quarterly public reporting. Algorithmic disclosure. Stakeholder access to governance records.
L5 — Oversight	Civilian review	Standing committee with civilian majority. Annual AI model re-validation. Binding authority on governance disputes. Two-thirds vote for policy changes.
L4 — Sovereign Security	Data protection	100% Canadian data residency. Zero cross-border transfer. PIPEDA/PHIPA compliance. AES-256 encryption. Hardware security module key management.
L3 — Risk & Fairness	Constraint enforcement	All AI advisory only. Pre-Execution Constraint Gate under 100 milliseconds. Bias testing quarterly. Statistical parity threshold under 10%. Prohibited capabilities enforced at code level.
L2 — Gatekeeping	Access and consent	Identity verification. Consent management. Progressive AI disclosure. Rate limiting. Anomaly detection.
L1 — Infrastructure	Compute foundation	Canadian sovereign cloud. Multi-region disaster recovery. Energy-efficient data centre selection. Platform security baseline.

2.1 Mathematical Properties

Decisiveness.

In the Condorcet majority-voting model, an odd-numbered governance body cannot produce a tie. Under standard assumptions, the probability of a split decision at n=7 is zero. This is not a design preference — it is a mathematical proof published by Condorcet in 1785 and cited continuously for 241 years.

Fault Tolerance.

In this governance design, up to three of the seven layers may fail while four continue to enforce correct decisions. Three of seven layers can be compromised — by incompetence, corruption, or external attack — and the remaining four still reach correct consensus, corresponding to a governance fault-tolerance analogue of 42.8%.

Scalability.

The Mersenne-style parity progression (3 → 7 → 15 → 31 → 63 → 127) preserves odd parity at every tier. A municipal deployment runs at seven. A provincial federation runs at fifteen. A national architecture runs at thirty-one. The mathematical guarantees hold at every scale.

3. Global Regulatory Alignment

MAEGM v1.6 maps to the five major global AI governance frameworks. The alignment is documented, verifiable, and available for independent review.

3.1 EU AI Act (Effective 2025, phased through 2027)

EU AI Act Requirement	MAEGM Coverage
Risk classification (unacceptable/high/limited/minimal)	L3 implements risk-tiered constraint application. L5 reviews classifications.
Prohibited practices (social scoring, biometric sorting)	L3 prohibits social scoring, emotional inference, and demographic profiling at code level. MAEGM goes further — prohibits ALL emotional inference, not just workplace.
High-risk system requirements	L3 risk assessment + L6 documentation + L7 human oversight + L1 data quality controls.
Transparency obligations	L2 progressive AI disclosure + L6 public reporting + advisory disclosure on all outputs.
Human oversight	L7 absolute veto + L5 standing committee + L2 operator override. Multi-layer human override exceeds the EU AI Act’s single-layer requirement.
Post-market monitoring	L3 continuous monitoring + L6 annual reporting + automated drift detection.

3.2 NIST AI Risk Management Framework (AI RMF 1.0, 2023)

NIST Function	MAEGM Coverage
GOVERN — Risk-aware culture	L7 Cabinet authority + L5 Standing Committee + MAEGM as constitutional document.
MAP — Context and risk categorization	L3 risk categorization + sector module mapping + algorithmic impact assessment.
MEASURE — Risk assessment	L3 fairness metrics (under 10% statistical parity) + bias testing + quarterly audits.
MANAGE — Risk treatment	L3 constraint enforcement + L5 committee review + L7 veto + four-tier escalation.

3.3 ISO/IEC 42001 (AI Management Systems)

MAEGM’s seven-layer architecture functions as an AI Management System (AIMS) that aligns with ISO 42001 requirements. The Plan-Do-Check-Act cycle maps directly: L5 reviews (Plan), L1-L2 implementation (Do), L3 monitoring (Check), L7 authority (Act). Formal ISO 42001 certification is eligible and will be pursued following operational pilot.

3.4 OECD AI Principles (2019, Updated 2024)

All five OECD principles are covered: inclusive growth and sustainable development (L5 diverse committee + equity frameworks), human-centred values and fairness (L7 human sovereignty + L3 fairness constraints), transparency and explainability (L6 public reporting + advisory disclosure), robustness and security (L4 sovereign security + L1 infrastructure), and accountability (L7 Cabinet authority + L5 enforcement + immutable audit logs).

3.5 UNESCO Recommendation on the Ethics of AI (2021)

All six UNESCO principles are covered: Do No Harm (L3 prohibitions + L7 halt authority + child protection architecture), human rights and dignity (L7 human sovereignty + L5 survivor and Indigenous seats), diversity and inclusiveness (L5 committee composition requirements), environmental sustainability (addressed in Environmental & Sustainability Playbook v2.0, March 2026 — Ontario 91% zero-carbon grid advantage, platform carbon footprint governance, WFH emissions integration), privacy (L4 sovereign security + PIPEDA compliance), and transparency (L6 full transparency layer + public reporting).

3.6 Alignment Summary

Framework	MAEGM Alignment	Notes
EU AI Act	8/8 requirements	Exceeds scope on emotional inference and multi-layer override
NIST AI RMF	4/4 functions	Exceeds specificity of implementation
ISO 42001	Eligible for certification	Certification to follow operational pilot
OECD Principles	5/5 principles	Exceeds accountability depth
UNESCO	6/6 principles	Environmental sustainability addressed in Playbook v2.0

4. Agentic AI Governance

As AI systems increasingly operate with delegated authority — automated compliance checks, scheduling, data processing, multi-agent workflows — governance must extend beyond static models to cover autonomous agents.

MAEGM v1.6 establishes an absolute authority ceiling for AI agents:

Agents MAY: analyze, summarize, flag, recommend, draft, calculate, monitor, and alert.

Agents MAY NOT: enforce, escalate, modify policy, contact citizens, authorize payments, alter governance, override human decisions, or self-modify capabilities.

This ceiling is non-negotiable. Any agent capability exceeding it requires L5 committee review and L7 human authorization before deployment. Multi-agent systems are limited to a maximum chain depth of three agents before a mandatory human checkpoint. Circular delegation between agents is architecturally blocked. All agent actions are logged with full provenance chains.

Every AI agent operating within MAEGM infrastructure has a unique, auditable cryptographic identity. No anonymous agents. No shared service accounts across agent boundaries. Agent lifecycle management — creation, approval, deployment, monitoring, decommissioning — is governed at L2 with L5 oversight.

5. Automated Ethical Drift Monitoring

Governance that only audits periodically will miss drift between audits. MAEGM v1.6 specifies continuous, automated monitoring for ethical drift, bias emergence, privacy degradation, and governance compliance.

Bias drift

Monitored continuously at L3: statistical parity ratio across all protected groups (threshold under 10% disparity), equalized odds differential (threshold under 5%), and calibration index (threshold plus or minus 3%). When thresholds are approached, automated alerts escalate to human review. When thresholds are breached, automated constraint tightening engages and the L5 Standing Committee is notified.

Privacy drift

Monitored continuously at L4: automated scanning for data residency violations on every API call, telemetry analysis for unauthorized outbound data patterns, consent validity monitoring, and metadata accumulation alerts when anonymized datasets approach re-identification thresholds.

Governance compliance

Monitored at L5 through L7: committee meeting cadence tracking, voting record completeness, conflict registry currency, and override log review.

The monitoring architecture transitions MAEGM from a static standard to a living governance system that detects and corrects drift in real time.

6. Cross-Platform Validation

MAEGM has been independently reviewed by fifteen AI platforms across six continents. Cross-validation proves internal logic coherence (no contradictions in the seven-layer architecture), structural consistency (layers interact correctly with no bypass paths), mathematical alignment (fairness formulas and scoring rubrics validated), and institutional viability (documentation at cabinet and enterprise grade).

Cross-validation does NOT prove operational deployment success (which requires pilot), political adoption (which requires government decision), or market revenue (which requires activation). This distinction is intentional. We do not conflate validation with endorsement.

7. What Is Open vs. Proprietary

7.1 Designated for Open-Source Release (Apache 2.0 — licensing in preparation)

The MAEGM seven-layer governance architecture, the agentic AI governance addendum, the global regulatory crosswalk, the automated ethical drift monitoring specification, and the child protection framework (O.C.T.O.P.U.S. — released under CC BY-NC 4.0 to prevent commercial exploitation of child safety governance).

Open-source release establishes BWR Group Canada as originator and primary authority. Apache 2.0 permits commercial use by others while requiring attribution. The governance standard gains value through adoption. The platform gains value through implementation exclusivity.

7.2 Proprietary (BWR Group Canada)

The MyBiz platform (all sector modules), scoring algorithms, financial models, revenue architecture, valuation documents, and commercial integration specifications.

8. The Mathematical Heritage

The governance architecture was not invented. It was assembled from solutions that eleven mathematicians, economists, engineers, and computer scientists produced across 241 years — none of whom knew they were contributing to the same structure.

Condorcet (1785) proved that odd-numbered groups cannot deadlock and that collective judgment improves with group size. Arrow (1951) proved that no single governance mechanism can satisfy all fairness criteria, which is why MAEGM uses seven overlapping layers rather than one. Sen (1998) proved that human welfare must be measured by capability, not income — the economic foundation for formalization. Mersenne (1644) provided the scaling sequence that preserves governance guarantees at every tier. Lamport (1982) proved that systems can reach correct consensus even when participants are compromised. Turing (1936) built the machine that made AI governance necessary. Hinton (2012) built the intelligence that runs on it.

The mathematics are 241 years old. The application is new. The architects deserve the credit. The assembler built what their blueprints described.

9. Architecture Status

Component	Status
Seven-layer architecture	100% complete — frozen
Global regulatory alignment	EU AI Act, NIST, ISO 42001, OECD, UNESCO — all mapped
Agentic AI governance	Complete — authority ceiling enforced
Ethical drift monitoring	Complete — continuous automated monitoring specified
Environmental sustainability	Addressed — Playbook v2.0 (March 2026)
Cross-platform validation	15 platforms confirmed, 6 continents
SOC 2 Type I	In preparation
ISO 42001 certification	Eligible — to follow operational pilot
Operational pilot	Planned — phased deployment

10. About BWR Group Canada

BWR Group Canada — MyBiz AI Division is based in Mississauga, Ontario. The MAEGM architecture governs MyBiz Ontario, a platform designed to formalize Canada’s $72.4 billion underground economy [VER — Statistics Canada, 2023] through universal business licensing, making home-based operators verifiable, insurable, and bankable.

The full thesis series examining AI governance through mathematics, film, and economic theory is published at brentai.ca/series. The visual storyboard showing the platform in action is available at brentai.ca/the-exhibit.

GOVERNANCE FRAMEWORK SPECIFICATION — NOT AN OPERATIONAL SYSTEM

Implementation requires regulatory consultation, audit engagement, municipal agreements, and phased pilot deployment.

BWR Group Canada — MyBiz AI Division

EGAN PRICE Standard — Named for H.E. Price — Boxing Day 1999

No ambiguities. No shortcuts. No drift.

© 2026 BWR Group Canada Inc. All Rights Reserved.

Governance framework components designated for open-source release under Apache 2.0 (licensing in preparation). Commercial platform components remain proprietary.