Why This Work Is Needed Now
Good intentions do not protect people. Policy does. Across industries and institutions, AI is being deployed faster than the frameworks needed to govern it responsibly. The mental health consequences of that gap are real and growing.
This initiative is dedicated to building the policy infrastructure that is currently missing: frameworks grounded in evidence, informed by decades of practitioner experience, and designed to be adopted by institutions that are ready to act.
The foundational lens for this work is the distinction between Digital Math (what AI systems measure) and Life Math (what actually matters to human beings). Every policy proposal developed here is evaluated against that standard.
About This Initiative
This is an emerging initiative led by Rochelle Newton, EdD, drawing on nearly five decades of technology leadership across healthcare, higher education, and enterprise environments. The frameworks and policy positions presented here represent the beginning of a larger body of work. Collaboration with researchers, policymakers, institutions, and community stakeholders is actively welcomed and sought.
Practitioner-Grounded
This work is built on direct experience leading technology implementation at the institutional level, including as Chief Operating Officer of Duke Health. The gap between policy intent and operational reality is well understood here.
Evidence-Oriented
Policy positions are developed from a foundation of research, data, and the documented experiences of workers, students, and communities navigating AI disruption. Advocacy without evidence is advocacy without credibility.
Equity-Centered
AI harm is not evenly distributed. Frameworks developed through this initiative explicitly address the disproportionate impact on communities of color, lower-income workers, and historically underserved populations.
Built for Implementation
The goal is not position papers that sit on shelves. Every framework developed here is written with institutional adoption in mind: clear language, defined standards, and realistic pathways to action.
The Policy Development Approach
This describes the methodology being applied as frameworks are developed. It is a working process, not a completed body of legislation or regulation.
Issue Identification and Research
Each focus area begins with identifying specific, documentable harm: where AI deployment is creating psychological risk, workforce instability, or unprotected vulnerability. Research synthesis and practitioner knowledge both inform this stage.
Framework Development
From identified issues, draft frameworks are developed with enough specificity to be actionable: proposed standards, definitions, accountability structures, and implementation considerations for institutions and policymakers.
Engagement and Review
Frameworks are designed to be tested and refined through conversation with policymakers, institutional leaders, mental health professionals, and the workers and communities most directly affected by AI disruption.
Advocacy and Dissemination
Completed frameworks are shared through speaking engagements, published writing, conference presentations, and direct engagement with institutions and public officials who have the authority to act on them.
Emerging Policy Positions
These positions are in active development and represent the initial framework for this work. They are shared here to invite dialogue, collaboration, and refinement.
Before deploying AI in consumer, healthcare, or workforce contexts, organizations should assess and document the potential psychological impact on the people affected. This initiative is developing a framework for what those assessments should include and who should conduct them.
There is currently no standardized mechanism for reporting or tracking psychological harm caused by AI systems. This initiative is developing a proposed framework for institutional accountability, modeled on existing workplace safety reporting structures.
When AI-driven changes eliminate or substantially alter roles, the psychological impact on affected workers is largely unaddressed. This work advocates for mental health support as a standard, expected component of any responsible AI workforce transition plan.
Children and adolescents require age-specific protections when interacting with AI systems. This initiative is developing a framework for minimum psychological safety standards for AI deployed in educational and youth-serving environments.