Frontier AI Election Evaluation Framework

I am developing a practical framework that adapts traditional election observation methods to test how AI systems perform during elections. With more voters using LLMs and AI-mediated search as primary information sources, we need a reliable way to monitor the risks. This project gives civil society and policymakers the evidence they need to develop policy recommendations and advocate for safe AI and reduce the risk that AI failures erode democratic legitimacy.

Over the next four months, I will build and pilot an open-access toolkit for domestic election monitors and democracy practitioners. The framework will include prompt libraries covering common election-related information requests, testing protocols, documentation templates, implementation guidance, and recommendations for interpreting and communicating findings. Because elections occur across diverse political and linguistic environments, the project is designed to produce an evaluation framework that can be adapted across contexts.

To build this, I’m combining classic election-monitoring principles with modern AI evaluation practices. I'll be refining the methodology alongside election experts, relationships I've built through a decade of work in elections, including representing the US government as an international election observer in countries like Azerbaijan and Kyrgyzstan. I will also consult experts in AI evaluations and model testing to ensure the methodology is statistically sound and avoids overstating conclusions.