发表机构
Carnegie Mellon University; Indiana University Indianapolis（卡内基梅隆大学; 印第安纳大学印第安纳波利斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过访谈12位美国残障用户，揭示其在对话式AI中按需披露残障、面临双重接收者规范冲突及记忆功能导致信息漂移，提出需增强情境控制以保护隐私。
AI 中文摘要
对话式AI助手会记住人们告诉它们的内容，而对于残障人士来说，这通常包括残障信息。我们访谈了12位使用基于LLM的助手（如ChatGPT、Claude和Gemini）的美国残障成年人，了解他们何时、如何以及为何向这些系统披露残障信息，并与向人类披露的情况进行比较。以情境完整性作为分析视角，我们发现参与者按需求而非按名称披露，将残障转化为任务范围内的指令；同一披露被两个接收者评判——一个不作评判的对话者和一个持有数据的公司——产生相反的规范；记忆功能减轻了重复披露的负担，却让残障信息漂移到不属于它的情境中。参与者进行了广泛的边界工作以恢复情境，并希望控制范围、来源、保留和访问，而非逐话语的开关。我们讨论了对话式AI助手设计的影响。
英文摘要
Conversational AI assistants remember what people tell them, and for disabled people, that often includes disability. We interviewed 12 adults with disabilities in the United States who use LLM-based assistants such as ChatGPT, Claude, and Gemini about when, how, and why they disclose disability to these systems and how this compares with disclosing to people. Using contextual integrity as an analytic lens, we found that participants disclosed by need rather than by name, translating disability into task-scoped instructions; that the same disclosure was judged against two recipients, a non-judging interlocutor and a data-holding company, producing opposite norms; and that memory features relieved the burden of repeated disclosure while letting disability information drift into contexts where it did not belong. Participants did extensive boundary work to restore context and wanted control over scope, provenance, retention, and access rather than per-utterance toggles. We discuss implications for the design of conversational AI assistants.
Comments16 pages, 1 figure, 3 tables. Under review
