Vermont AI Mental Health Law Spotlights Rubber-Stamping Risk

Vermont has joined a growing number of states enacting legislation to govern AI-powered mental health tools, but the new law highlights a critical vulnerability in how such regulations are structured.

The state's H.816 law requires that a licensed mental health professional review and approve recommendations generated by AI chatbots before they reach patients. The mandate mirrors similar provisions in other state laws and appears straightforward: human expertise serves as a checkpoint for machine-generated advice.

Yet the legislation's lack of specificity about what constitutes adequate review creates a significant gap, according to an analysis first reported by Forbes contributor Lance Eliot.

The automation bias problem

The central concern is that therapists may fall into a pattern of reflexively approving AI outputs without thorough examination—a phenomenon known as automation bias. Mental health professionals working under time pressure or facing high caseloads could begin treating AI recommendations as inherently reliable, conducting only cursory reviews rather than critical assessments.

This risk is compounded by the vague language in current regulations. Vermont's law, like those in other states, does not specify the depth of analysis required, the time a reviewer must spend, or the documentation needed to demonstrate meaningful oversight. Without concrete standards, both AI developers and supervising therapists may lack clear accountability if harmful advice reaches a patient.

Why it matters

As AI mental health tools proliferate, the gap between regulatory intent and enforcement mechanisms could undermine patient safety. Vague oversight requirements may create liability shields rather than genuine protection—allowing companies to claim compliance while therapists perform minimal review. For healthcare organizations evaluating AI mental health platforms, this regulatory ambiguity means internal protocols must exceed statutory minimums to ensure patient welfare and manage institutional risk.

The path to stronger safeguards

Eliot argues that effective regulation requires more detailed requirements. Potential improvements could include mandatory review timeframes, documentation standards showing substantive engagement with AI outputs, and specific criteria for when therapists must override or modify AI recommendations.

The challenge extends beyond Vermont. As states continue crafting AI healthcare legislation, the question of how to enforce meaningful human oversight—rather than performative compliance—remains unresolved. Without clearer standards, the promise of professional review as a safeguard may prove hollow in practice.

The analysis was published by Lance Eliot in Forbes, examining Vermont's approach as representative of broader trends in state-level AI mental health regulation.