Model AI Legislation Framework (Quick Orientation)
A practical overview and “how to use” guide for legislative staff
Purpose
This brief is intended for legislative aides, counsel, and committee staff reviewing the Model AI Legislation Framework and its supporting materials. It provides a concise explanation of what the framework does, how it is structured, and how it may be used during legislative development.
The Framework
This framework provides a risk-based approach for governing artificial intelligence systems that interact directly with people and may influence judgment, behavior, or emotional well-being.
It is designed to:
- Prevent avoidable harm
- Preserve innovation and flexibility
- Avoid technology lock-in
- Avoid surveillance or content regulation
- Provide clear accountability when harm occurs
It does not regulate ideas, speech, research, or internal model design.
Why It Exists
Conversational and human-interactive AI systems have already caused documented harm due to the absence of basic safety infrastructure. Unlike other safety-critical technologies, these systems were deployed without standardized risk analysis, documentation, or independent review.
This framework applies existing safety principles—used in aviation, medicine, and engineering—to AI systems that affect people directly.
How the Framework Is Structured
The framework is organized into three tiers, each serving a distinct role:
Executive Summary
Explains why safety oversight is necessary and why conversational AI is the starting point. Establishes scope and intent.
Tier 1 – Foundational Framework
Defines what must be addressed in law:
- Risk-based assessment
- Documentation
- Oversight
- Accountability
This tier is the governing layer.
Tier 2 – Technical Basis
Explains how risk assessment works in practice using established engineering methods.
This tier is informative, not binding.
Tier 3 – Adoption & Implementation Guidance
Explains how legislatures and agencies may implement Tier 1 without overreach or rigidity.
This tier provides direction, not mandates.
Imperative vs. Flexible
Conditions Required for Effectiveness (once adopted):
Without these conditions, safety oversight becomes ineffective and largely symbolic.
- Covered systems require risk-based assessment
- Failure modes are identified and documented
- Proportional mitigation is initialized
- Documentation is made available for review
Flexible:
- Which technical methodology is used
- Which standards body is referenced
- How agencies structure review and certification
- Timing and rollout during transition periods
Role of Certification
Certification serves as the principal compliance mechanism under the framework.
It:
- Demonstrates good-faith risk management
- Supports market access and procurement
- Informs liability and enforcement decisions
- Does not approve content or ideas
Government does not run certification; it recognizes acceptable certification processes.
What This Framework Avoids
- No new centralized AI authority
- No monitoring of private conversations
- No access to training data or model internals
- No content moderation mandates
- No single required tool or vendor
Key Takeaway for Aides
This framework gives legislators a way to:
- Require responsibility without micromanagement
- Protect the public without slowing innovation
- Establish accountability without surveillance
- Rely on documentation instead of trust alone
How to Use This Framework
For Legislators, Staff, and Reviewers
This insert explains how to work with the Model AI Legislation Framework during drafting, review, and implementation.
Step 1: Start with Tier 1
Tier 1 is the governing document.
Use it to:
- Define which AI systems are covered
- Establish mandatory risk assessment and documentation
- Set enforcement principles and safe-harbor protections
Tier 1 language may be incorporated directly, adapted, or referenced by statute.
Step 2: Reference Tier 2 for Technical Understanding
Tier 2 is a technical companion, not statutory text.
Use it to:
- Understand what “risk-based assessment” means
- Evaluate whether proposed compliance methods are credible
- Brief members or leadership on technical feasibility
Do not embed Tier 2 language directly into statute.
Step 3: Use Tier 3 to Shape Implementation
Tier 3 explains:
- How agencies may operationalize Tier 1
- How certification can be recognized
- How oversight can occur without surveillance
- How innovation remains protected
Tier 3 helps avoid:
- over-specification
- technology lock-in
- unintended enforcement consequences
Step 4: Understand the Role of AI-FMEA
AI-FMEA is:
- An example of an accepted risk-assessment method
- A structured way to identify and prioritize failure modes
- Not mandatory and not exclusive
AI-FMEA may be referenced as:
- Illustrative guidance
- Evidence of good-faith compliance
- A model for equivalent approaches
Step 5: During Review or Hearings
When evaluating proposals or testimony, ask:
- Does this system interact directly with people?
- Could it influence judgment, behavior, or emotional state?
- Has a structured risk assessment been performed?
- Is documentation available?
- Were foreseeable harms addressed?
These questions align directly with Tier 1 obligations.
Step 6: During Enforcement or Incident Review
Use documentation to determine:
- Whether risks were foreseeable
- Whether they were identified
- Whether reasonable mitigation was attempted
The framework supports proportional accountability, not automatic punishment.
Key Principle to Remember
“This framework regulates risk management, not technology itself.“
It is designed to evolve with AI, protect the public, and preserve innovation—without requiring lawmakers to become technical experts.
Version 1.1 – December 2025
Printed or downloaded copies may not reflect the most current revision. The authoritative version is maintained at aisafetyinternational.com.
© 2025 AI Safety International.
This document may be freely shared, referenced, and adapted for educational, policy, and legislative purposes, provided proper attribution is maintained. No endorsement is implied.