Draft:Marshall AI Governance Readiness Standard
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Marshall AI Governance Readiness Standard (MAGRS)
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The Marshall AI Governance Readiness Standard (MAGRS) is a proposed governance framework designed to help organizations manage the risks and responsibilities associated with the use of artificial intelligence (AI) systems. It emphasizes human accountability, operational visibility, and defined boundaries for AI-assisted decision-making, particularly in small and medium-sized businesses (SMBs).
MAGRS is based on the principle that while artificial intelligence may assist in decision-making, responsibility for outcomes remains with human operators.
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Overview
MAGRS was developed to address what its creator describes as a “responsibility gap” in AI adoption, where organizations deploy AI systems without clearly defining oversight, accountability, or operational constraints.
The framework is structured to be practical and enforceable rather than theoretical, focusing on real-world implementation within organizations that may lack dedicated AI governance teams.
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Core Principles
MAGRS is built on three primary pillars:
Visibility
Organizations must be able to observe and understand AI system behavior, including inputs, outputs, and decision processes. This includes logging, monitoring, and auditability.
Boundaries
AI systems must operate within clearly defined limits. These boundaries specify where AI can and cannot be used, particularly in high-risk or decision-critical contexts.
Accountability
Each AI system must have an assigned human owner responsible for its outcomes. Accountability is explicitly defined rather than assumed.
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Operational Model
MAGRS promotes a structured lifecycle for AI adoption: 1. Assessment before deployment – evaluating risks and use cases prior to implementation 2. Defined system boundaries – restricting AI usage to approved contexts 3. Assignment of responsibility – designating accountable individuals 4. Ongoing monitoring – maintaining visibility into system behavior 5. Periodic review – conducting regular evaluations and updates
Organizations are encouraged to perform quarterly reviews to ensure continued compliance and effectiveness.
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Agent Primaries Concept
MAGRS incorporates the concept of “agent primaries,” which define the core functional roles of AI systems: • Intent Capture – interpreting user input • Decision Logic – applying rules and determining actions • Execution – performing tasks or triggering workflows • Observation – recording system activity • Accountability Binding – linking actions to human responsibility
These roles are intended to ensure that AI systems are structured, auditable, and aligned with governance requirements.
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Certification Model
MAGRS proposes a credentialing system for organizations: • Onboarded status – initial participation with limited access • Credentialed status – full recognition upon meeting reporting and governance requirements • Suspended status – applied when organizations fail to maintain compliance
Credentialing is intended to reflect demonstrated operational discipline rather than self-declared compliance.
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Target Audience
MAGRS is primarily aimed at: • Small and medium-sized businesses (SMBs) • Organizations adopting AI without formal governance structures • Consultants and integrators implementing AI systems
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Philosophy
The framework is grounded in what is referred to as the “Marshall Principle”:
“Artificial intelligence may assist human decision-making, but responsibility always remains with humans.”
MAGRS positions itself as an independent governance model, emphasizing neutrality, auditability, and enforceable standards.
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See also • Artificial intelligence governance • AI ethics • Risk management • IT governance
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External links • Official website: https://magrs.org (redirects to marshall.net)