AI-enabled biological misuse may emerge through multiple interacting pathways rather than a single linear chain of events. This project will build a literature-grounded scenario library, map the causal links and decision-relevant variables within each scenario, and use Bayesian inference (a quantitative risk estimation method developed by Safer AI) to compare how alternative pathways could amplify or mitigate biological risk.
About the project
This project will develop a structured library of AI-enabled biological misuse scenarios drawn from the existing literature. For each scenario, the team will identify decision-relevant variables, causal relationships, intervention points, and alternative pathways through which risk could emerge or escalate.
The project will then translate selected scenarios into causal pathway models and Bayesian networks to compare how different assumptions, safeguards, and interacting pathways affect overall risk. It will also make a methodological contribution by extending the mentor’s previous research on AI-enabled biosecurity risks to a transparent approach for translating qualitative scenarios into quantitative risk estimates. The goal is to understand how multiple pathways may combine to amplify biological risk rather than evaluating each pathway in isolation.
Theory of change
Transformative AI could increase biological risk by lowering expertise barriers, accelerating technical workflows, and connecting capabilities across multiple stages of a misuse pathway. Existing assessments often evaluate these capabilities separately, which can miss how alternative pathways interact or compound risk.
This project will provide a structured method for moving from literature-grounded misuse scenarios to causal models and Bayesian risk estimates. By identifying key variables, uncertainties, intervention points, and high-risk pathways, the work can help researchers and policymakers prioritize evaluations, safeguards, monitoring systems, and governance responses.
Your role
Mentees will work as research collaborators and help scope the project, refine the research questions, review relevant literature, build the scenario library, identify decision-relevant variables and causal links, and support the development of causal pathway models and Bayesian networks. They will receive hands-on mentorship across the full research pipeline and have ownership over clearly defined workstreams.
The project is exploratory, so mentees should be self-driven, communicate their availability clearly, and participate actively in team discussions. They will be expected to provide regular progress updates, raise challenges early, and coordinate closely with the mentor and other team members.
Prerequisites
Applicants should be proficient in Python and comfortable working with open-source models, Hugging Face Inference Providers, Pydantic, PyTorch, and related libraries. They should have experience conducting structured literature reviews and writing technical research papers, preferably for academic conferences. Familiarity with the complex system fundamentals, causal loop mapping, thematic coding, and quantitative risk estimation method will be a plus
Familiarity with biosecurity, or biological misuse research, is helpful but not required. Applicants should also be comfortable using Git, maintaining reproducible research workflows, and translating qualitative evidence into structured variables and models.
Application question(s)
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Provide a link to a first-authored or lead-authored technical research paper that you have published, presented, or submitted to a conference or journal. Briefly describe your contribution to the research design, implementation, analysis, and writing. (150 words)
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Spend no more than 30 minutes outlining a small experiment for the proposed study of AI-enabled biological misuse pathways. State one hypothesis, the objective, one research question, specific methods, and the expected outcome. Identify the data or literature sources, the methods you will use to investigate the research question, the computational requirements, the major limitations, and a realistic six-week timeline. We are primarily evaluating your research design skills and awareness of existing methods, rather than expecting a fully developed proposal. (500 words maximum)
About the mentor
Dr. Swaptik Chowdhury is an AI policy researcher and mixed-methods scientist focused on technical AI governance. His work examines how quantitative risk modeling and decision-making under deep uncertainty can help researchers and policymakers understand the societal risks posed by advanced AI systems. His recent research includes studying science communication as a complex system, examining safeguards against the malicious use of LLM agents in bioinformatics, and developing structured threat-modeling methods to identify and analyze potential AI loss-of-control pathways. Swaptik is a data scientist at Dun & Bradstreet and has taught statistics at Loyola Marymount University.