_This project is one of three winners of the "Outstanding Governance Project" prize for our AI Governance (August 2024) course. The text below is an excerpt from the final project._

Recently, while studying AI governance, policy, and regulation in BlueDot Impact's AI Governance course I became absorbed in reading about the practice of AI model red teaming (AIMRT), an adversarial practice to probe weaknesses and discover unknown capabilities in advanced AI models. As I come from a bioethics and global health policy background, I immediately perceived parallels with gain-of-function research (GoFR) in the field of virology — a field notorious for its high-risk experiments on pathogens. Both domains grapple with balancing innovation and safety. As I considered these practices side-by-side I began to draw more analogies between them. One dissimilarity struck me; it occurred to me that GoFR is considered rather dangerous and controversial and as a result is highly regulated. Meanwhile, to my knowledge, AIMRT has received nothing but positive regard and encouragement even in policy and regulation circles. I found the apparent discrepancy in perception and treatment of these seemingly deeply analogous practices intriguing. I wondered whether the analogies I perceived were merely superficial, or if there were lessons that could be learned from GoFR for safer and better-regulated practice in the sphere of AIMRT. I decided to take a closer look.

_To view the full project submission, click [here](https://www.linkedin.com/pulse/analogous-risks-lessons-ai-model-red-teaming-from-roberts-phd-qxqac/)._


Analogous Risks and Lessons for AI Model Red Teaming from Gain-of-Function Virology Research

Aaron Roberts • 1st Place • December 2024