As artificial intelligence (AI) continues to transform numerous sectors, behavioral healthcare stands on the precipice of a profound technological revolution. Recent investments—amounting to billions of dollars from both public and private sources—have fueled rapid development and deployment of AI systems designed to either augment or in some cases replace the roles traditionally held by skilled behavioral health providers. This explosive growth raises critical questions not merely about the efficacy or safety of AI tools, but fundamentally about governance: who truly decides how, when, and to what end AI should be integrated within behavioral health services? Emerging research highlights a striking imbalance in decision-making power, revealing how private sector entities have dominated the shaping of AI’s future in this deeply sensitive field, often sidelining those most intimately connected to the outcomes—the service users, the public, and the providers themselves.
AI’s incursion into behavioral healthcare is driven by advances in machine learning, natural language processing, and computational psychiatry, which enable the analysis of complex behavioral data at unprecedented scale and speed. These technologies promise to fill critical gaps in mental health services, addressing shortages of providers and enhancing diagnostic accuracy. However, much of the discourse has fixated on whether these AI tools work reliably and safely, with comparatively little attention paid to who is setting priorities or defining ethical boundaries. The dominant narrative, propelled largely by startups and technology firms, has centered on efficiency, scalability, and innovation metrics, sidelining deeper reflection on the societal and human dimensions of care.
Private companies are uniquely well-positioned to marshal extensive capital and technological expertise, allowing them to commercialize AI applications swiftly. Yet this advantage also cements their disproportionate influence over the trajectory of AI in behavioral health. Their incentives skew toward product development timelines, market viability, and intellectual property protection rather than participatory governance. Consequently, the conceptualization of behavioral health challenges and solutions often reflects corporate interests rather than the nuanced needs and lived experiences of service users and practitioners. This power imbalance risks marginalizing critical voices, resulting in tools that may not resonate with or adequately support the complexities of human behavior and mental wellness.
Moreover, public investment, though substantial, has not translated into equally prominent public oversight or engagement mechanisms. This gap raises questions about democratic accountability, given that many AI systems are ultimately funded by taxpayers. Without explicit frameworks for involving community stakeholders, patients, and clinical experts in decision-making processes, the development and deployment of AI risks becoming opaque, with limited opportunities to scrutinize or contest the underlying algorithms, data sources, or clinical premises. The absence of inclusive deliberation undermines trust and could exacerbate health disparities if AI tools reflect or amplify biases embedded in their training data or design choices.
Central to these challenges is the conceptual tension between AI as a technological innovation and behavioral healthcare as a deeply human-centered practice. Behavioral health involves intricate therapeutic relationships, nuanced clinical judgments, and individualized care pathways that resist simple codification. AI’s promise to “supplement or replace” provider roles must be reconciled with the ethical imperative to preserve empathy, dignity, and agency for service users. This requires reframing AI not as a silver bullet but as one element within a collaborative ecosystem shaped by multiple stakeholders with diverse expertise and perspectives.
The need for democratizing AI development and deployment in behavioral healthcare is urgent and multifaceted. First, it demands creating inclusive governance structures that prioritize the voices of service users, clinical providers, and the broader public. Participatory design approaches and community advisory boards can facilitate iterative feedback loops ensuring AI tools address real-world needs and concerns. Second, transparency must be enhanced regarding how AI algorithms operate, including clear communication about their limitations, potential biases, and decision criteria. Third, regulatory frameworks must evolve beyond traditional medical device approval to incorporate ethical, social, and cultural dimensions specific to behavioral health contexts.
Inclusion of behavioral health providers in AI development promises numerous benefits. Clinicians possess critical contextual knowledge about patient behaviors, therapeutic processes, and systemic barriers—insights invaluable for designing AI applications that are clinically relevant and ethically sound. Their involvement can mitigate risks of overreliance on automated recommendations and promote safeguards against compromising therapeutic rapport. Similarly, empowering service users to shape AI tools fosters respect for personal agency, cultural diversity, and lived experiences, promoting equity and responsiveness.
Public engagement extends beyond individual stakeholders to encompass society-wide debates about acceptable uses of AI in mental health. Questions arise around data privacy, especially given the sensitivity of behavioral health information and the risks of stigmatization or discrimination. Debates must also tackle issues of access and digital divides, ensuring AI innovations do not exacerbate existing inequities due to socioeconomic, racial, or geographic factors. Such societal dialogues are essential to establish trust and legitimacy for behavioral health AI initiatives.
Importantly, the economics of AI in behavioral healthcare warrant critical scrutiny. The commercialization models favored by private sector actors may prioritize scalability and profitability over therapeutic efficacy and patient well-being. This dynamic can lead to oversimplified, one-size-fits-all solutions that neglect the heterogeneity of mental health conditions and patient needs. Instead, funding and policy efforts should encourage responsible innovation grounded in therapeutic effectiveness, ethical integrity, and equitable access.
Ongoing research and policy initiatives are beginning to recognize these governance challenges, advocating for a shift in power dynamics toward multi-stakeholder collaboration. Interdisciplinary partnerships among technologists, clinicians, ethicists, patients, and public representatives are essential to co-create AI systems aligned with shared values and health goals. Moreover, fostering digital literacy and capacity among behavioral health providers and service users can empower informed engagement with these emerging technologies.
As AI becomes an increasingly integral component of behavioral health ecosystems, the stakes of governance decisions grow ever higher. Failure to democratize AI development risks entrenching systemic biases, diminishing care quality, and eroding public trust. Conversely, embedding inclusive, transparent, and ethical deliberation at the core of AI innovation holds the promise to transform behavioral healthcare for the better—enhancing access, precision, and personalization while honoring human dignity and agency.
The future of AI in behavioral healthcare will be shaped not just by algorithms or investment figures, but fundamentally by who is at the table when critical decisions are made. Achieving a balanced, equitable, and humane integration of AI demands dismantling the current disproportionate influence of private interests and centering the needs and voices of the people behavioral health is meant to serve. Only through such democratic governance can AI fulfill its transformative potential as a tool that supports rather than supplants the deeply personal art of mental health care.
The evolving dialogue around AI governance in behavioral healthcare serves as a crucial exemplar for other sectors wrestling with similar tensions between innovation, ethics, and democratic accountability. Lessons learned here could pave the way for a new paradigm where powerful technologies advance collective well-being through genuinely inclusive and participatory frameworks, rather than top-down corporate agendas. The critical challenge—and opportunity—lies in reimagining how society governs its most intimate technologies, ensuring they serve people first and foremost.
In sum, AI-driven advances offer extraordinary opportunities to enhance behavioral health services but simultaneously pose profound ethical and governance dilemmas. Addressing these requires bold commitments to democratize AI development by involving service users, clinicians, and the public in shaping tools that are safe, effective, equitable, and respectful of human complexity. Only through such collective stewardship can the promise of AI be realized in a manner that honors the values at the heart of mental health care.
Subject of Research: Governance and democratization of artificial intelligence technologies in behavioral healthcare.
Article Title: Empowering service users, the public, and providers to determine the future of artificial intelligence in behavioral healthcare.
Article References:
Last, B.S., Khazanov, G.K. Empowering service users, the public, and providers to determine the future of artificial intelligence in behavioral healthcare. Nat. Mental Health (2026). https://doi.org/10.1038/s44220-025-00565-6
Image Credits: AI Generated
