As we try to mitigate AI risks, we will face tradeoffs among risk categories, for instance between misuse risks and extreme power concentration. As we navigate these tradeoffs it might be useful to measure the preferences of the public/ AI experts/ other constituencies regarding how to trade off these risks.
About the project
Claims that a policy or research portfolio reduces "AI risk" conceal a weighting across distinct threats — cyber and biological misuse, extreme power concentration, and loss of control. This project makes this weighting an object of measurement. We build an AI Risk Index: a weighted mean over four risk categories, with weights elicited from structured surveys, using the choice-experiment methods health economics uses to build QALY valuations from public preferences. Respondents allocate a fixed budget of tokens across interventions that reduce each risk, with token effectiveness varied across rounds so that the underlying preference weights can be identified. Because respondents' beliefs about each risk's likelihood are elicited separately, differences in priorities can be decomposed into disagreements about facts and disagreements about values — the central output. The identical instrument is designed for three samples: the general public, AI safety researchers, and professional philosophers. Within the fellowship, the project could be a proof of concept: to design and validate the full instrument and run it on simulated (AI-generated) respondents, benchmarked against existing human polling, leaving a pre-registered, costed human study ready to field. The deliverables would be a working paper, a reusable survey, and a fielding plan — and the first evidence on whether simulated respondents can stand in for humans on questions about AI risk.
Theory of change
At the macro-strategy level, allowing us to think about how to trade-off various risk categories, and building the foundation to constructing a risk index that can be traded off against other considerations, such as the plausible benefits of AI development and deployment.
Your role
scoping out experimental economics literature, survey design, running surveys/ simulated surveys
Prerequisites
Background in economics or psychology, solid programming background. Experience with running surveys or social science lab experiments preferred.
Application question(s)
What are some of the key concerns you have with using surveys to assess people's risk preferences?
About the mentor
I’m an academic economist at the University of Edinburgh. My traditional research fields have been international trade and economic geography, but in the last couple of years I’ve been gradually transitioning into the Economics of AI (with a focus on the growth implications of transformative AI). I was involved in Epoch AI’s GATE project, one of the first integrated assessment models of the AI transition. I'm currently interested in: (1) modelling the AI value chain; (2) modelling the impact of AI on the cross-country distribution of income and power; (3) the risk-return tradeoff of AI regulation (with a particular focus on the expected economic costs of various approaches to regulating AI development and deployment); (4) modelling social preferences around AI-related risks; (5) the effects of AI on social science, and particularly on the production vs verification balance.