Recent gain in popularity of AI conversational agents has led to their increased use for improving productivity and supporting well-being. While previous research has aimed to understand the risks associated with interactions with AI conversational agents, these studies often fall short in capturing the lived experiences. Additionally, psychological risks have often been presented as a sub-category within broader AI-related risks in past taxonomy works, leading to under-representation of the impact of psychological risks of AI use. To address these challenges, our work presents a novel risk taxonomy focusing on psychological risks of using AI gathered through lived experience of individuals. We employed a mixed-method approach, involving a comprehensive survey with 283 individuals with lived mental health experience and workshops involving lived experience experts to develop a psychological risk taxonomy. Our taxonomy features 19 AI behaviors, 21 negative psychological impacts, and 15 contexts related to individuals. Additionally, we propose a novel multi-path vignette based framework for understanding the complex interplay between AI behaviors, psychological impacts, and individual user contexts. Finally, based on the feedback obtained from the workshop sessions, we present design recommendations for developing safer and more robust AI agents. Our work offers an in-depth understanding of the psychological risks associated with AI conversational agents and provides actionable recommendations for policymakers, researchers, and developers.

翻译：近年来，AI对话代理日益普及，被广泛用于提升生产力和促进心理健康。尽管已有研究试图理解与AI对话代理交互相关的风险，但这些研究往往未能充分捕捉用户的实际生活体验。此外，在过往的风险分类研究中，心理风险常被归为更广泛的AI相关风险的子类别，导致AI使用心理风险的影响未能得到充分体现。为应对这些挑战，本研究提出了一种新颖的风险分类法，重点关注通过个体生活体验收集到的使用AI的心理风险。我们采用混合方法，包括对283名具有心理健康生活体验的个体进行综合调查，并邀请生活体验专家参与研讨会，以构建心理风险分类体系。我们的分类体系涵盖了19种AI行为、21种负面心理影响及15种与个体相关的使用情境。此外，我们提出了一种基于多路径情境案例的新型框架，用于理解AI行为、心理影响与个体用户情境之间复杂的相互作用。最后，基于研讨会获得的反馈，我们提出了设计建议，以开发更安全、更稳健的AI代理。本研究深入探讨了与AI对话代理相关的心理风险，并为政策制定者、研究人员和开发者提供了可操作的建议。