Effective response to emergencies (such as a nascent pandemic) relies on having trustworthy knowledge infrastructure. However, AI reduces the cost of producing convincing false information at scale and as AI becomes more integrated into public and private systems there is significant risk of knowledge infrastructure and decision-making becoming compromised by AI-generated materials and AI itself becoming an artificial hivemind.
About the project
We are interested in two dimensions of this problem. First, how AI capabilities change the threat landscape. Earlier generations of false health information required human effort to produce and spread. However, AI enables automation, personalization, and scale that outpace current detection and response mechanisms. Understanding the specific capabilities that create new risks and the rate at which those capabilities are advancing is essential for mitigating risk.
Second, the combination of disinformation campaigns with outbreaks or (hypothetical) bioweapons releases. This includes examining how malicious actors might time and target disinformation to maximize disruption, what signatures might distinguish coordinated campaigns from organic misinformation spread, and how attribution challenges complicate response decisions.
For mentees that are not biosecurity focused, we also welcome them to submit their own ideas related to the general topic.
Expected output will depend on mentees exact interests, but would likely be a short piece to publish in an independent publication in the science-and-tech policy space. A longer version of the research behind the short piece could be self-published or submitted to a journal if time and mentee capacity permits.
Theory of change
Proactively working to understand specific risk pathways related to AI production and incorporation of false information into knowledge infrastructure could help reduce AI safety issues such as scalable deception, model homogenization, and epistemic security.
Your role
Very autonomous, 1 check-in/week
Prerequisites
We are open to a broad range of applicants, but expect this project to be well-suited to social scientists, historians, or policy researchers interested in epistemic risks from AI, particularly misinformation and disinformation risks. If you have a technical background and would like to do a technical project it would be beneficial for you to propose your idea for how to apply your technical skills to this topic. An interest in assessing a biosecurity (pandemic) scenario for risk would be of interest but is not required.
Location preference
Being able to take US PT (up to 7pm) meetings
Application question(s)
Please explain in under 200 words why you're interested in this project and how your background and skills will help make this project a success.
About the mentors
Natalie Linton is a biosecurity professional experienced in infectious disease epidemiology, statistical modeling, threat modeling, risk assessment, and pandemic preparedness. She has worked for state-level government for the past 5 years and recently was a Summer Fellow with the Centre for Governance of AI (GovAI).
Sarah studies how frontier AI systems shape the way people form beliefs and make decisions. Her route into this was building the systems themselves: as a software engineer at Google, she led agent personalization for Maps, shipping generative AI to millions of users, which gave her a close view of how design and personalization choices quietly change what people see and trust. She's now a seasonal fellow at GovAI, where she treats model specifications (the public documents labs publish describing how their models should behave) as auditable governance artifacts, and asks how they influence users' capacity to reason well for themselves. The mechanisms she keeps coming back to are persuasion, cognitive offloading, and lock-in. Sarah studied Computer Science and Statistics at Harvard.
Sarah made the move from engineering into governance recently and can offer support in the transition. She likes working with people who want research that is technically grounded and legible to policymakers, and she'll push you toward writing that a non-specialist can follow. Sarah also cares about work that reaches past the field to teachers and to ordinary people navigating a messy information environment. If you're drawn to AI literacy, epistemic risk, evaluations, or public communication, she'd like to hear what you want to build!