About Us

The AI Oversight & Governance Workgroup (AIOG) 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.

Our Team

Sandra Soo-Jin Lee, PhD

Workgroup Lead

Chief of the Division of Ethics and Professor of Medical Humanities of Ethics

Noémie Elhadad, PhD

Chair, Department of Biomedical Informatics

Associate Professor of Biomedical Informatics

Chris H. Wiggins, PhD

Associate Professor of Applied Mathematics and Systems Biology

Namita Azad, MS, MPH

Senior Director, Organizational Development-Transformation

Ashley Halinsky, MS

Assistant Director of IRB Management

Edward Huang

Senior Director, AI and Digital Transformation

Despina Kontos, PhD

Vice-Chair of AI and Data Science Research, Radiology

Chief Research Information Officer (CRIO), CUIMC

Paul Kurlansky, MD

Professor, Department of Surgery

Miriam Laugesen, PhD

Associate Professor of Health Policy and Management

Harry Reyes Nieva, PhD, MAS

Postdoctoral Research Scientist, Division of Infectious Diseases

Elise Zheng, PhD

Postdoctoral Fellow

Juana Becerra, PhD Candidate

Research Associate in AI Ethics

Core Principles for AI Oversight and Governance

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.
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.
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.
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.
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.
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.