The AI Action Plan's focus on self-regulation shifts compliance responsibility directly to your organization. You can't wait for federal regulations to dictate what's acceptable; you need to build your own governance framework now. This checklist helps you establish the foundational controls and processes required when self-regulation becomes the norm.
Prerequisites
Before using this checklist, ensure you have:
- Executive sponsorship: Self-regulation requires investment in controls without a regulatory mandate. You need board or C-suite backing for budget and resources.
- Cross-functional AI governance committee: Include legal, compliance, IT, data privacy, and business unit representatives. Self-regulation doesn't work in silos.
- AI system inventory: Document all AI systems in production, development, or pilot phases before implementing controls.
Checklist Items
1. Define Your AI Risk Taxonomy
☐ Create a classification system that categorizes AI systems by risk level (high, moderate, low) based on decision impact, data sensitivity, and potential harm.
Requirement reference: While the AI Action Plan advocates minimal federal interference unless "unduly restrictive to innovation," you still need internal risk thresholds.
What good looks like: Your taxonomy includes specific criteria. For example, any AI system making credit decisions or processing special categories of data automatically qualifies as high-risk. Business units can self-assess new systems using your criteria without needing compliance review for every pilot.
2. Establish AI Development Standards
☐ Document mandatory requirements for AI system design, including data quality standards, model validation procedures, and bias testing protocols.
What good looks like: Your standards specify that high-risk AI systems require independent validation before production deployment. Developers know exactly what testing documentation they need to provide, and you have a clear approval gate.
3. Implement Model Governance Controls
☐ Create a model risk management framework covering model development, validation, implementation, and ongoing monitoring.
Requirement reference: In the absence of prescriptive federal rules, you're building the control framework that regulators would have mandated.
What good looks like: Every production AI model has a designated owner, documented validation evidence, and scheduled revalidation dates. You can produce a complete model inventory with risk ratings in under an hour.
4. Build an AI Incident Response Plan
☐ Develop procedures for identifying, escalating, and remediating AI system failures, including bias incidents, accuracy degradation, and security breaches.
What good looks like: Your plan defines what constitutes an AI incident. For example, model accuracy drops below 85% on validation data, or bias testing reveals disparate impact exceeding 20%. Response procedures specify who gets notified, investigation timelines, and remediation approval authorities.
5. Create Transparency Documentation
☐ Establish requirements for AI system documentation that explains how models work, what data they use, and how decisions are made.
What good looks like: For each high-risk AI system, you maintain documentation that a non-technical executive could read and understand the system's purpose, data sources, and decision logic. This documentation gets reviewed and updated quarterly.
6. Implement Ongoing Monitoring
☐ Deploy technical controls that track AI system performance, data drift, and prediction accuracy in production environments.
Requirement reference: Self-regulation only works if you're actively watching for problems. Monitoring isn't optional.
What good looks like: You receive automated alerts when model performance degrades beyond acceptable thresholds. Your monitoring dashboard shows real-time accuracy metrics for all high-risk AI systems, and you review it weekly.
7. Establish Vendor AI Governance
☐ Extend your AI governance framework to third-party AI systems, including vendor risk assessments and contractual requirements.
What good looks like: Your vendor risk profile template includes specific AI questions about model validation, bias testing, and data lineage. Contracts with AI vendors include audit rights and require notification of material model changes.
8. Document Risk-Based Decision Rationale
☐ Create a decision log that records why you classified systems at specific risk levels and what controls you applied.
Requirement reference: When regulators eventually show up, they'll ask why you thought your approach was reasonable. Document your thinking now.
What good looks like: For each AI system, you have a written justification explaining its risk classification and the controls you implemented. If you accepted residual risk, you documented why and who approved it.
9. Build AI Ethics Guidelines
☐ Develop principles for ethical AI use that address fairness, transparency, accountability, and human oversight.
What good looks like: Your ethics guidelines translate into specific requirements. For example, high-risk AI decisions require human review before final implementation, or you prohibit AI systems from making decisions about individual rights without appeal mechanisms.
10. Establish Training Requirements
☐ Implement mandatory AI governance training for developers, data scientists, and business users deploying AI systems.
What good looks like: Training completion is tracked in your learning management system. Developers can't deploy AI systems to production without completing the training, and you have technical controls enforcing this requirement.
Common Mistakes
Treating self-regulation as optional: Some organizations interpret minimal federal interference as permission to ignore AI risks entirely. Self-regulation means you're responsible for building the controls that protect your organization and stakeholders. If you don't regulate yourself, someone else will eventually do it for you.
Copying someone else's framework without customization: Your AI risk profile isn't identical to other organizations. A framework designed for a consumer lending platform won't work for a healthcare diagnostics company. Build controls that match your specific risk exposure.
Focusing only on discrimination and bias: Yes, fairness matters, but self-regulation also covers accuracy, security, privacy, and operational resilience. Don't let bias testing consume all your governance bandwidth while ignoring other material risks.
Waiting for perfect documentation: You won't build a complete AI governance program in three months. Start with high-risk systems, implement basic controls, and iterate. Perfectionism is the enemy of progress in self-regulation environments.
Ignoring state-level requirements: The AI Action Plan's emphasis on minimal federal interference doesn't eliminate state laws. You still need to comply with California's AI transparency requirements, Colorado's consumer protection rules, and any other applicable state regulations.
Next Steps
Self-regulation requires continuous improvement, not one-time compliance. After completing this checklist:
- Schedule quarterly governance reviews: Reassess your AI risk taxonomy as technology evolves and new use cases emerge.
- Track emerging state regulations: Monitor state-level AI laws that might create new obligations for your organization.
- Benchmark against industry practices: Join industry working groups developing AI standards. Self-regulation works better when industries coordinate on common frameworks.
- Prepare for eventual federal rules: Self-regulation is the current approach, but regulatory winds shift. Build your framework assuming someone will eventually audit your decisions.
The absence of prescriptive federal rules doesn't mean you're operating in a vacuum. It means you're responsible for determining what "reasonable" AI governance looks like for your organization. This checklist gives you the foundation; now you need to build the controls that match your risk profile.
