Oversight and Governance

The AI Oversight & Governance Working Group at the Vagelos College of Physicians and Surgeons is dedicated to ensuring that AI technologies are developed and deployed in ways that uphold human dignity, well-being, and the broader public good.

We establish governance frameworks that promote responsible innovation and safeguard health and biomedical data integrity. Rooted in values-driven design, accountability, human-centered AI, transparency, and sustainability, our work focuses on identifying processes to promote responsible decision-making and mitigate risks. Through cross-disciplinary collaboration, public engagement, and proactive oversight, we strive to create AI systems that advance biomedical knowledge, enhance patient care, uphold public trust in healthcare technology, and align with institutional and societal needs.

Core Principles for AI Oversight and Governance

1. Values-Driven Design – AI systems should be developed and governed with a deep commitment to human dignity and well-being, ensuring they enhance rather than undermine individual and societal flourishing. Governance frameworks should align AI with the broader public good, prioritizing ethical decision-making and social benefit.
2. Accountability & Safety – AI governance should proactively identify and address risks by establishing adaptable oversight structures that ensure reliability, security, and responsible data stewardship. This includes enforcing compliance with institutional policies and legal standards while protecting sensitive data through rigorous privacy and security measures.
3. Robustness & Generalizability – AI governance should actively promote robustness of tools, minimize distortions and adjust for skewed data, ensure access, and foster systems that serve all patients.
4. Human-centered – AI governance should be designed and implemented in ways that preserves autonomy and human control over the design and use of AI tools.
5. Transparency & Engagement – AI governance should foster meaningful outreach, education, and public engagement to ensure AI development aligns with societal needs and values. This requires cross-disciplinary dialogue within the university and active collaboration with researchers, clinicians, policymakers, and the public.
6. Sustainability – AI governance should prioritize responsible innovation that minimizes environmental impact and promotes ethical stewardship of data and computational resources. Sustainability commitments emphasize the need for ongoing evaluation and should support long-term societal well-being and future generations.