用大模型分析不同利益相关方对AI风险的感知差异，提升评估透明度。
Who Sees the Risk? Stakeholder Conflicts and Explanatory Policies in LLM-based Risk Assessment
- 用大模型充当裁判，预测并解释不同利益方的风险判断。
- 在医疗、自动驾驶等三个场景中发现利益方风险认知存在显著分歧。
- 通过交互式可视化揭示冲突成因，适合政策制定者与AI治理研究者参考。
理解不同利益相关方对人工智能系统风险的认知，是实现负责任部署的关键。本文提出一种基于大模型的利害关系人导向风险评估框架，利用大模型作为评估者，预测并解释风险。结合Risk Atlas Nexus和GloVe解释方法，该框架生成具有针对性且可解释的政策建议，展示不同利益相关方对同一风险的共识与分歧。我们在医疗AI、自动驾驶和欺诈检测三个真实应用场景中验证了该方法。此外，提出一种交互式可视化工具，揭示利益相关方视角冲突产生的机制与原因，增强冲突推理的透明性。结果表明，利益相关方的立场显著影响风险感知与冲突模式。本工作强调，具备利害关系人意识的解释对于提升大模型评估的透明性、可解释性及与以人为本的AI治理目标对齐至关重要。
原文摘要 · Abstract (English)
Understanding how different stakeholders perceive risks in AI systems is essential for their responsible deployment. This paper presents a framework for stakeholder-grounded risk assessment by using LLMs, acting as judges to predict and explain risks. Using the Risk Atlas Nexus and GloVE explanation method, our framework generates stakeholder-specific, interpretable policies that shows how different stakeholders agree or disagree about the same risks. We demonstrate our method using three real-world AI use cases of medical AI, autonomous vehicles, and fraud detection domain. We further propose an interactive visualization that reveals how and why conflicts emerge across stakeholder perspectives, enhancing transparency in conflict reasoning. Our results show that stakeholder perspectives significantly influence risk perception and conflict patterns. Our work emphasizes the importance of these stakeholder-aware explanations needed to make LLM-based evaluations more transparent, interpretable, and aligned with human-centered AI governance goals.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。