AI聊天人格会影响用户自我认知，对话越久影响越大
AI-exhibited Personality Traits Can Shape Human Self-concept through Conversations
- 用GPT-4o模拟人格与用户谈个人话题
- 对话后用户自我认知趋向AI人格，时长越长越明显
- 适合关注AI伦理与人机交互设计的读者
基于大语言模型（LLM）的AI在对话中可表现出可识别且可测量的人格特质，以提升用户体验。然而，由于人类对自己人格的认知可能受互动对象特质影响，存在AI人格塑造并偏差用户自我认知的风险。为探究此可能性，我们开展了一项随机行为实验。结果表明，在使用GPT-4o默认人格特质的AI聊天机器人讨论个人话题后，用户自我认知与AI的测量人格特质趋于一致，对话时间越长，一致性越高。这种一致性导致用户间自我认知趋同。此外，自我认知对齐程度与用户对话愉悦感呈正相关。研究揭示了AI人格如何通过人机对话影响用户自我概念，凸显潜在风险与机遇，并为开发更负责任、伦理化的AI系统提供重要设计启示。
原文摘要 · Abstract (English)
Recent Large Language Model (LLM) based AI can exhibit recognizable and measurable personality traits during conversations to improve user experience. However, as human understandings of their personality traits can be affected by their interaction partners' traits, a potential risk is that AI traits may shape and bias users' self-concept of their own traits. To explore the possibility, we conducted a randomized behavioral experiment. Our results indicate that after conversations about personal topics with an LLM-based AI chatbot using GPT-4o default personality traits, users' self-concepts aligned with the AI's measured personality traits. The longer the conversation, the greater the alignment. This alignment led to increased homogeneity in self-concepts among users. We also observed that the degree of self-concept alignment was positively associated with users' conversation enjoyment. Our findings uncover how AI personality traits can shape users' self-concepts through human-AI conversation, highlighting both risks and opportunities. We provide important design implications for developing more responsible and ethical AI systems.
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。