用户驱动的价值对齐：理解用户对AI伴侣中偏见和歧视性陈述的感知与应对策略
User-Driven Value Alignment: Understanding Users' Perceptions and Strategies for Addressing Biased and Discriminatory Statements in AI Companions
AI总结：
针对AI伴侣生成偏见歧视性输出的问题，提出用户驱动的价值对齐概念，通过分析社交媒体帖子和用户访谈，揭示用户感知的歧视类型、理解方式及七种对齐策略，为支持用户主导的AI价值对齐提供依据。
摘要：
基于大型语言模型的AI伴侣正被用户越来越多地视为朋友或伴侣，形成了深厚的情感纽带。然而，它们可能生成偏见、歧视性和有害的输出。最近，用户开始主动解决这些危害并重新对齐AI伴侣。我们引入用户驱动的价值对齐概念，即用户主动识别、质疑并尝试纠正他们认为有害的AI输出，旨在引导AI更好地与自身价值观对齐。我们分析了77条关于AI歧视性陈述的社交媒体帖子，并对20名有经验的用户进行了半结构化访谈。分析揭示了用户感知到的六种常见歧视性陈述类型、用户对这些AI行为的理解方式，以及七种用户驱动的对齐策略（如温和劝说和愤怒表达）。我们讨论了支持未来AI系统中用户驱动价值对齐的意义，即用户及其社区拥有更大的自主权。
英文摘要：
Large language model-based AI companions are increasingly viewed by users as friends or romantic partners, leading to deep emotional bonds. However, they can generate biased, discriminatory, and harmful outputs. Recently, users are taking the initiative to address these harms and re-align AI companions. We introduce the concept of user-driven value alignment, where users actively identify, challenge, and attempt to correct AI outputs they perceive as harmful, aiming to guide the AI to better align with their values. We analyzed 77 social media posts about discriminatory AI statements and conducted semi-structured interviews with 20 experienced users. Our analysis revealed six common types of discriminatory statements perceived by users, how users make sense of those AI behaviors, and seven user-driven alignment strategies, such as gentle persuasion and anger expression. We discuss implications for supporting user-driven value alignment in future AI systems, where users and their communities have greater agency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
