发表机构
Nokia Bell Labs; University of Nottingham; Politecnico di Torino（诺基亚贝尔实验室; 诺丁汉大学; 都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出三阶段构思工具，用LLM模拟利益相关者并构建小世界网络，通过介数中心性排序风险，提升风险新颖性且不损可信度，实验组识别更多系统性风险。
AI 中文摘要
参与式AI风险评估的构思阶段通常从空白或有限的预定义风险列表开始，这使得难以揭示间接或系统性危害。为解决这一局限，我们提出了一种三阶段构思支持工具。该工具补充而非取代参与式AI，并有助于将后续与受影响社区的互动聚焦。首先，它根据给定的AI用途动态发现利益相关者，并递归向外扩展，使被忽视或间接的利益相关者得以浮现。其次，它用大语言模型模拟这些利益相关者，将其连接成给定拓扑的网络，并让他们对风险进行头脑风暴。第三，它使用网络中心性度量对风险进行优先级排序。在初步评估中，我们发现，通过小世界网络连接的智能体运行介数中心性效果最佳，因为它提升了由连接不相交群体的利益相关者提出的风险，揭示了传统方法常常遗漏的新颖系统性危害。在一个AI聊天机器人伴侣用例中，与单一LLM头脑风暴相比，该方法将识别风险的新颖性提高了约1.1分，与智能体LLM头脑风暴相比提高了0.5分，这是在归一化的五点李克特量表上测量的，且未降低识别风险的可信度或严重性。为测试我们的框架是否有助于使用未来车轮方法的人工主导构思会议，我们将11个非西方年轻聊天机器人用户团队分为两类：对照组（团队）和实验组（团队），进行参与式AI风险评估。对照组从初步评估中45位AI从业者生成的风险列表开始；实验组从我们框架生成的列表开始。实验组总体上识别了更多风险，以及更多系统性、人机交互和环境风险。
英文摘要
The ideation phase of participatory AI risk assessment often starts with a blank slate or a limited list of predefined risks, making it difficult to surface indirect or systemic harms. To address this limitation, we propose a three-stage ideation support tool. The tool complements participatory AI, rather than replacing it, and helps focus later engagement with affected communities. First, it dynamically discovers stakeholders depending on the given AI use and recursively expanding outward, allowing overlooked or indirect stakeholders to emerge. Second, it simulates these stakeholders with LLMs, connecting them into a network of a given topology, and having them ideate about risks. Third, it prioritizes risks using network centrality measures. In an initial evaluation, we found that betweenness centrality run through agents connected in a small-world network works best as it elevates risks raised by stakeholders who bridge disconnected groups, surfacing novel, systemic harms that traditional methods often miss. On an AI chatbot companion use case, this approach increased the novelty of the identified risks by approximately 1.1 points over single LLM brainstorming, and by 0.5 points over agentic LLM brainstorming, measured on a normalized five-point Likert scale, without reducing the plausibility or severity of the identified risks. To test whether our framework helps a human-led ideation session using the Futures Wheel approach, we divided 11 teams of non-western young chatbot users into two types: control (team) and treatment (team) in a participatory AI risk assessment. The control teams started from a list of risks generated by the 45 AI practitioners in the initial evaluation; the treatment teams started from a list generated by our framework. The treatment teams identified more risks overall, and more systemic, human-computer interaction, and environmental risks.
Comments19 pages, 5 figures
