Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment
This addresses the problem of improving foresight for AI systemic risks for policymakers and researchers, offering a hybrid workflow to enhance risk assessment, though it is incremental in combining existing methods.
The paper tackled the challenge of assessing long-term systemic risks of AI by introducing a scalable approach using AI agents to simulate consequences via the Futures Wheel method, applied to four AI uses, resulting in agents generating 86-110 consequences condensed into 27-47 unique risks per use, with benchmarking against human evaluations from 290 experts and 84 participants showing agents produced more systemic risks.
AI impact assessments often stress near-term risks because human judgment degrades over longer horizons, exemplifying the Collingridge dilemma: foresight is most needed when knowledge is scarcest. To address long-term systemic risks, we introduce a scalable approach that simulates in-silico agents using the strategic foresight method of the Futures Wheel. We applied it to four AI uses spanning Technology Readiness Levels (TRLs): Chatbot Companion (TRL 9, mature), AI Toy (TRL 7, medium), Griefbot (TRL 5, low), and Death App (TRL 2, conceptual). Across 30 agent runs per use, agents produced 86-110 consequences, condensed into 27-47 unique risks. To benchmark the agent outputs against human perspectives, we collected evaluations from 290 domain experts and 7 leaders, and conducted Futures Wheel sessions with 42 experts and 42 laypeople. Agents generated many systemic consequences across runs. Compared with these outputs, experts identified fewer risks, typically less systemic but judged more likely, whereas laypeople surfaced more emotionally salient concerns that were generally less systemic. We propose a hybrid foresight workflow, wherein agents broaden systemic coverage, and humans provide contextual grounding. Our dataset is available at: https://social-dynamics.net/ai-risks/foresight.