Disabled people navigate privacy when sharing info with AI assistants
When Disability Disclosure Travels: Memory, Privacy, and Contextual Integrity in Conversational AI
Human-Computer InteractionComputers and Society
Summary
People with disabilities often tell AI chat helpers about their disability so these systems can assist better. The researchers talked to disabled adults who use AI assistants and found that they share disability details only when needed, not by name. They treat the AI like it won’t judge, but worry about the company that stores the info. They want ways to control what the AI remembers and who can see their disability information. The study suggests new ways to design AI that respect privacy and context.
What this means in practice
- •For conversational ai developers: Design memory features that let users control how disability information is stored, accessed, and limited by context in AI assistants.
- •For privacy policy teams: Create data policies for conversational AI that balance remembering user disability disclosures with protecting privacy in different interaction contexts.
Authors
Atieh Taheri, Mahya Tazike, Patrick Carrington, Jeffrey P. Bigham
Abstract
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.