Governing AI for Mental Health: Fragmented State Approaches and the Case for a Federal Framework

Area of Science:

• Health Law
• Technology Governance
• Mental Healthcare Policy

Background:

• The absence of federal guidelines has led to diverse state-level regulations for artificial intelligence (AI) in mental health.
• This has resulted in a fragmented regulatory environment across the United States.

Purpose of the Study:

• To identify and analyze the distinct governance approaches US states are employing to regulate AI in mental health.
• To examine the limitations inherent in these varied state-level regulatory strategies.

Main Methods:

• A comparative case analysis of statutory texts from four states: Illinois, Utah, New York, and Nevada.
• Identification of two primary regulatory approaches: regulating AI in clinical contexts and regulating the technology itself.

Main Results:

• Two main governance approaches were identified: regulating AI within clinical settings and regulating the technology directly.
• Some states combine these approaches, but divergent strategies create uneven consumer protections and regulatory uncertainty for stakeholders.
• The study highlights that neither approach alone adequately addresses the risks of AI in mental health due to its dual clinical and consumer applications.

Conclusions:

• Current state-level AI mental health regulations are fragmented and insufficient.
• A coordinated, risk-based federal regulatory framework is essential for consistent consumer protection and to reduce uncertainty for AI developers and clinicians.
• Addressing the complexities of AI in mental health requires a unified national approach.