Governing AI at the clinical threshold: a grounded theory framework for responsible AI deployment in mindfulness-based mental health

Abstract

AI-powered platforms for mental health delivery are scaling rapidly into contexts previously governed by trained human practitioners—without empirically grounded frameworks to guide their ethical deployment, manage adverse effects risk, or address the structural design failures that make them harmful to vulnerable populations. This constructivist grounded theory study examined how expert mindfulness clinicians and teachers conceptualize governance failures, societal harms, and design risks in AI-mediated mental health delivery. Fourteen practitioners—representing six contemplative and clinical traditions, with experience ranging from 8 to 60 years—were recruited via purposive and theoretical sampling and interviewed using a semi-structured protocol. Data were analyzed through iterative open, axial, and selective coding by a four-member team, with theoretical saturation confirmed at N = 13. Analysis generated the Bounded Convergence Framework (BCF), a three-zone governance model organizing AI deployment into an inner zone of irreducibly human clinical functions, a practitioner-governed boundary zone defined by three non-negotiable threshold conditions, and an outer zone of legitimate AI-assisted functions. Sixteen governance principles were identified, with particular implications for contraindication screening, crisis escalation architecture, AI identity disclosure, and the protection of adolescent and trauma-exposed populations. The BCF offers the first empirically grounded governance framework for AI deployment in mindfulness-based mental health delivery, with direct implications for platform developers, health service administrators, regulators, and clinical practitioners navigating this rapidly expanding and underregulated domain.