AI 中文总结
该研究开发了AI智能体多层框架，基于2078项工作任务生成风险场景并验证，扩展出15类职场AI风险分类法，揭示了不同部署模式的风险差异，为职场AI风险管控提供了分类工具与依据。
AI 中文摘要
为了预判AI智能体带来的社会技术风险，组织需要分类法对其进行归类。然而，现有的AI风险分类法聚焦于广泛的风险，未涵盖智能体引入的特定于工作的风险。为填补这一空白，我们做出三项主要贡献：其一，通过对AI智能体相关文献的综述，开发了一个多层框架，该框架对智能体、目标和环境这三个核心组件及其相互作用进行建模；其二，将该框架嵌入结构化提示词中，应用于O*NET数据库的2078项工作任务描述，生成了8356个按严重程度和部署模式（自动化或增强）标注的风险场景，我们通过10个职业的45名工人及一名独立大语言模型（LLM）评判者验证了这些场景的合理性及其与工作任务的契合度；其三，我们扩展了现有分类法，创建了包含15个类别的职场AI智能体风险分类法，覆盖所有生成的风险场景。我们的分析得出四项发现：第一，增强模式并非固有安全，因为过度依赖智能体会逐渐削弱工人的技能和监督能力；第二，“智能体错误行动”占风险场景的比例最大，且严重风险的集中度最高，许多此类风险出现在人机边界处；第三，自动化模式主要与组织风险相关，而增强模式主要与工人风险相关；第四，在风险分类任务中，工人发现我们的分类法比另外两种分类法更易用，且在与近期生成式AI风险分类法的非平局比较中，有64%的情况偏好我们的分类法。这些发现表明，职场AI智能体风险并非仅由智能体单独产生，还取决于人们与智能体的协作方式以及智能体的部署方式，更安全的工作场所不仅需要更安全的智能体，还需要精心设计的人机智能体协作模式。
英文摘要
To anticipate socio-technical risks from AI agents, organizations need taxonomies to classify them. However, existing AI risk taxonomies focus on broad risks and do not capture job-specific risks introduced by agents. To address this gap, we make three main contributions. First, we developed a multi-layer framework from a literature review of AI agents. The framework models three core components and their interactions: agents, goals, and environment. Second, we embedded this framework in a structured prompt and applied it to descriptions of 2,078 job tasks from the O*NET database, producing 8,356 risk scenarios labeled by severity and deployment mode (automation or augmentation). We validated these scenarios with 45 workers across 10 job roles and an independent LLM judge, confirming their plausibility and alignment with job tasks. Finally, we extended an existing taxonomy to create a 15-category taxonomy of workplace AI agent risks that covers all our risk scenarios. Our analysis highlights four findings. First, augmentation is not inherently safe because overreliance on agents can gradually erode workers' skills and oversight. Second, Erroneous Agent Actions accounts for the largest share of risk scenarios and has the highest concentration of severe risks. Many arise at the human-agent boundary. Third, automation is associated mainly with organizational risks, while augmentation is associated mainly with risks to workers. Fourth, workers found our taxonomy easier to use for a risk classification task than two other taxonomies and preferred it in 64% of non-tied comparisons with a recent generative AI risk taxonomy. These findings show that workplace AI agent risks do not arise from agents alone; they also depend on how people work with agents and how agents are deployed. Safer workplaces require not only safer agents but also carefully designed human-AI agent collaboration.
