AI Governance Policy
Effective: February 1, 2026
Purpose
This Artificial Intelligence Governance Policy (the "Policy") establishes the principles, controls, and accountability framework governing the design, development, deployment, and operation of artificial intelligence and machine learning systems ("AI Systems") by Grid Interface, LLC ("Company").
This Policy is intended to support lawful, ethical, and responsible AI practices and align with applicable U.S. state artificial intelligence, consumer protection, and data protection laws, including the New York Responsible AI Safety and Education Act (RAISE Act), California automated decision-making and privacy regulations (CPRA/ADMT), and the Colorado Artificial Intelligence Act and similar frameworks ("State AI Laws").
2. Scope
This Policy applies to:
• All AI Systems developed, licensed, deployed, or operated by the Company
• All employees, contractors, and agents involved in AI-related activities
• All third-party models or tools integrated into Company products
3. Guiding Principles
Lawfulness
AI Systems will be designed and operated in compliance with applicable laws and regulations.
Fairness & Non-Discrimination
Reasonable measures will be taken to identify, test, and mitigate unlawful or prohibited bias.
Transparency
Appropriate disclosures regarding AI use, purpose, and limitations will be provided.
Human Oversight
Meaningful human review will be available for high-risk AI Systems.
Accountability
Clear ownership and escalation paths will exist for AI-related decisions.
Security & Privacy
AI Systems will be developed and operated using appropriate data protection and security safeguards.
4. AI System Inventory & Risk Classification
The Company shall maintain an inventory of all AI Systems, documenting:
• Intended purpose and decision context
• Data sources and dependencies
• Whether outputs materially affect legal, economic, or similarly significant rights
Each AI System shall be classified as low-risk, moderate-risk, or high-risk, with risk classification reviewed upon material modification, retraining, or expansion of use cases.
5. AI Risk & Impact Assessments
Prior to deployment of any moderate-risk or high-risk AI System, the Company shall conduct a documented AI risk and impact assessment addressing:
• Foreseeable discrimination or disparate impact risks
• Data quality, provenance, and representativeness
• Privacy, security, and misuse risks
• Degree of human reliance on AI outputs
• Availability of human review or override mechanisms
Risk assessments shall be updated upon material system changes.
6. Data Governance & Model Development Controls
The Company shall implement data governance practices that include:
• Documented data sourcing and provenance
• Review of training, validation, and testing datasets for representational imbalance or proxy discrimination
• Data minimization and purpose limitation principles
• Secure handling of training and inference data
7. Bias Testing & Mitigation
The Company shall implement documented bias testing procedures proportionate to the risk level of each AI System, which may include:
• Statistical fairness and outcome disparity analysis
• Error rate comparisons across relevant populations
• Counterfactual or sensitivity testing
• Human review of sampled outputs
Where bias risks are identified, reasonable mitigation measures shall be implemented and documented, including data refinement, feature constraints, retraining, or output calibration.
8. Human Oversight & Escalation
For high-risk AI Systems, the Company shall:
• Define circumstances requiring human-in-the-loop review
• Provide override or appeal mechanisms where appropriate
• Train reviewers on intervention criteria
• Log overrides and corrective actions
9. Transparency & Customer Communications
The Company shall provide customers with clear, accurate information regarding:
• The use of AI Systems in products or services
• The intended purpose and appropriate use of AI Systems
• Known limitations and material risks
• Availability of human review mechanisms where applicable
The Company shall avoid representations that AI Systems are error-free or bias-free.
10. Ongoing Monitoring & Model Lifecycle Management
The Company shall monitor AI Systems post-deployment to:
• Detect performance degradation, bias emergence, or model drift
• Identify feedback loops affecting training data
• Trigger retraining, modification, or decommissioning where necessary
Monitoring frequency shall be proportionate to system risk and impact.
11. Third-Party & Customer Controls
Where AI Systems rely on third-party models or are configured by customers, the Company shall:
• Conduct reasonable diligence on third-party AI providers
• Allocate AI governance responsibilities contractually
• Prohibit unsupported or unlawful high-risk uses
• Cooperate with customer and regulatory audits consistent with confidentiality obligations
12. Training, Accountability & Governance Structure
The Company shall:
• Designate responsible personnel or committees for AI governance oversight
• Provide periodic training to relevant employees on AI risk and compliance obligations
• Maintain documentation sufficient to demonstrate compliance with this Policy and applicable State AI Laws
13. Regulatory Cooperation & Policy Review
The Company shall reasonably cooperate with lawful regulatory inquiries related to AI Systems. This Policy shall be reviewed periodically and updated to reflect changes in law, technology, and industry standards.
14. No Guarantee of Outcomes
AI Systems are probabilistic by nature. This Policy does not require error-free or bias-free outcomes, but rather the implementation of reasonable, good-faith, and risk-based governance measures.
This Policy is intended for internal governance and external diligence purposes and does not create third-party beneficiary rights.