Research AI & Training Services

Service overview

We partner with institutions to design, implement and optimize oversight frameworks that align with ethical principles, regulatory requirements and best practices for AI/ML research — while building internal expertise through targeted training and continuing education.

AI & ML research oversight services

AI/ML-powered research often challenges traditional classifications, risk assessment methods, and regulatory pathways. We help your institution interpret the regulatory landscape, apply clear oversight frameworks, and make defensible decisions across the lifecycle of AI-enabled research.

• Regulatory classification guidance — determine when AI/ML tools and studies qualify as research, human subjects research, exempt activities, or QA/QI, aligned with the Common Rule and other applicable standards.
• QA/QI determinations — structured review support that differentiates quality assurance or improvement activities from research requiring full IRB oversight.
• IRB review frameworks for AI/ML studies — proven, field-tested frameworks with evaluation criteria, decision pathways and standardized review questions, customized to your institution.
• Risk assessment strategies — identify and evaluate risks including model bias, algorithmic opacity, data governance challenges and participant harms, with mitigation strategies.
• Privacy & data protection compliance — compliant data governance strategies consistent with HIPAA, the Common Rule and other applicable requirements.
• FDA device determination support — early regulatory strategy for research involving AI that may meet the FDA’s definition of a device, including risk categorization and planning for FDA interactions.
• Protocol review & feedback — expert review of AI and other novel technology protocols, improving clarity, compliance and defensibility before they reach your IRB.

IRB training & capacity building

Strong oversight depends on well-trained IRB members and staff who can apply thoughtful, consistent judgment across a broad range of research modalities. Our training programs strengthen review skills, enhance regulatory understanding, and build confidence in evaluating both AI-enabled and traditional research.

• Emerging technologies training — focused education on AI/ML, digital health platforms, wearable sensors, adaptive algorithms and other emerging research technologies.
• Reviewer training for complex protocols — practical, scenario-based training that sharpens reviewers’ ability to evaluate high-complexity studies.
• AI & non-AI protocol review skills — workshops and interactive sessions reinforcing core human subject protections principles and regulatory interpretation.
• Privacy & data protection — practical guidance for the large datasets AI/ML research depends on.