支持智能体的前瞻性分析以应对AI系统性风险：智能体用于广度，专家用于判断
Agent-Supported Foresight for AI Systemic Risks: AI Agents for Breadth, Experts for Judgment
AI总结：
本文提出了一种结合智能体和专家的前瞻性分析方法，用于评估AI系统的长期系统性风险，通过模拟智能体和专家讨论，扩大风险覆盖范围并提供上下文基础。
摘要：
AI影响评估通常强调短期风险，因为人类判断在更长的时间范围内会退化，这体现了Collingridge困境：前瞻性分析在知识最匮乏时最为需要。为解决长期系统性风险，我们引入了一种可扩展的方法，利用战略前瞻性方法中的未来轮方法模拟智能体。我们将其应用于四种AI应用，涵盖技术成熟度等级（TRLs）：聊天机器人伴侣（TRL 9，成熟）、AI玩具（TRL 7，中等）、哀伤机器人（TRL 5，低）和死亡应用（TRL 2，概念性）。在每种应用中进行30次智能体运行，智能体产生了86-110种后果，压缩成27-47种独特风险。为了将智能体输出与人类观点进行基准测试，我们收集了290名领域专家和7名领导者的评估，并与42名专家和42名普通公众进行了未来轮会议。智能体在不同运行中生成了多种系统性后果。与这些输出相比，专家识别出的风险较少，通常较少系统性但被认为更可能，而普通公众则提出了更多情感上重要的关切，这些关切通常较少系统性。我们提出了一种混合前瞻性工作流程，其中智能体扩大系统性覆盖范围，而人类提供上下文基础。我们的数据集可在：https://social-dynamics.net/ai-risks/foresight获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
