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

• URL: http://arxiv.org/abs/2602.08565v1
• Date: Mon, 09 Feb 2026 12:03:49 GMT
• Title: Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment
• Abstract summary: We introduce a scalable approach that simulates in-silico agents using the strategic method of the Futures Wheel.<n>We applied it to four AI uses spanning Technology Readiness Levels (TRLs): Companion (TRL 9, mature), AI Toy (TRL 7, medium), Griefbot (TRL 5, low), and Death App (TRL 2, conceptual)
• Score: 45.98986947599775
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

Related papers

• Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts [115.34779081791287]
  We report results from a three-round Delphi study conducted late 2025 with 272 international AI experts.<n>Experts rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.
  arXiv  Detail & Related papers  (2026-06-03T06:14:41Z)
• Frontier AI Risk Management Framework in Practice: A Risk Analysis Technical Report v1.5 [61.787178868669265]
  This technical report presents an updated and granular assessment of five critical dimensions: cyber offense, persuasion and manipulation, strategic deception, uncontrolled AI R&D, and self-replication.<n>This work reflects our current understanding of AI frontier risks and urges collective action to mitigate these challenges.
  arXiv  Detail & Related papers  (2026-02-16T04:30:06Z)
• GT-HarmBench: Benchmarking AI Safety Risks Through the Lens of Game Theory [8.38197391178077]
  We introduce GT-HarmBench, a benchmark of 2,009 high-stakes scenarios spanning game-theoretic structures.<n>We measure sensitivity to game-theoretic prompt framing and ordering, and analyze reasoning patterns driving failures.<n>Our results highlight substantial reliability gaps and provide a broad standardized testbed for studying alignment in multi-agent environments.
  arXiv  Detail & Related papers  (2026-02-12T17:29:52Z)
• Understanding Risk and Dependency in AI Chatbot Use from User Discourse [4.1957094635667875]
  We present a large-scale computational thematic analysis of posts collected between 2023 and 2025 from two communities, r/AIDangers and r/ChatbotAddiction.<n>We identify 14 recurring thematic categories and synthesize them into five higher-order experiential dimensions.<n>Our findings reveal five empirically derived experiential dimensions of AI-related psychological risk grounded in real-world user discourse.
  arXiv  Detail & Related papers  (2026-02-10T02:16:57Z)
• Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse [50.87630846876635]
  We develop nine detailed cyber risk models.<n>Each model decomposes attacks into steps using the MITRE ATT&CK framework.<n>Individual estimates are aggregated through Monte Carlo simulation.
  arXiv  Detail & Related papers  (2025-12-09T17:54:17Z)
• When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems [78.04679174291329]
  We introduce a proof-of-concept to simulate the risks of malicious multi-agent systems (MAS)<n>We apply this framework to two high-risk fields: misinformation spread and e-commerce fraud.<n>Our findings show that decentralized systems are more effective at carrying out malicious actions than centralized ones.
  arXiv  Detail & Related papers  (2025-07-19T15:17:30Z)
• Towards Evaluating Proactive Risk Awareness of Multimodal Language Models [39.44421152112219]
  A proactive safety artificial intelligence (AI) system would work better than a reactive one.<n>PaSBench evaluates this capability through 416 multimodal scenarios.<n>Top performers like Gemini-2.5-pro achieve 71% image and 64% text accuracy, but miss 45-55% risks in repeated trials.
  arXiv  Detail & Related papers  (2025-05-23T04:28:47Z)
• A Proposal for Evaluating the Operational Risk for ChatBots based on Large Language Models [39.58317527488534]
  We propose a novel, instrumented risk-assessment metric that simultaneously evaluates potential threats to three key stakeholders.<n>To validate our metric, we leverage Garak, an open-source framework for vulnerability testing.<n>Results underscore the importance of multi-dimensional risk assessments in operationalizing secure, reliable AI-driven conversational systems.
  arXiv  Detail & Related papers  (2025-05-07T20:26:45Z)
• Fully Autonomous AI Agents Should Not be Developed [50.61667544399082]
  This paper argues that fully autonomous AI agents should not be developed.<n>In support of this position, we build from prior scientific literature and current product marketing to delineate different AI agent levels.<n>Our analysis reveals that risks to people increase with the autonomy of a system.
  arXiv  Detail & Related papers  (2025-02-04T19:00:06Z)
• HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions [95.49509269498367]
  We present HAICOSYSTEM, a framework examining AI agent safety within diverse and complex social interactions.<n>We run 1840 simulations based on 92 scenarios across seven domains (e.g., healthcare, finance, education)<n>Our experiments show that state-of-the-art LLMs, both proprietary and open-sourced, exhibit safety risks in over 50% cases.
  arXiv  Detail & Related papers  (2024-09-24T19:47:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.