Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment

TL;DR

This paper introduces a scalable in-silico agent-based approach using the Futures Wheel to identify long-term systemic AI risks, complemented by human expert evaluation, to improve foresight beyond near-term assessments.

Contribution

It presents a novel hybrid foresight workflow combining AI agents and human judgment to better identify and evaluate systemic AI risks over long time horizons.

Findings

Agents produced extensive systemic risk scenarios.

Humans identified fewer but more likely risks.

Laypeople highlighted emotionally salient concerns.

Abstract

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.