When Disability Disclosure Travels: Memory, Privacy, and Contextual Integrity in Conversational AI

A recent research study investigated how individuals with disabilities in the United States interact with large language model (LLM)-based conversational AI, focusing on the disclosure of disability information. The study found that users often disclose disability by translating it into task-specific instructions rather than explicit identification, driven by immediate needs. A key observation was the tension between perceiving AI as a non-judgmental conversational partner and a data-holding entity, leading to conflicting norms around disclosure. The research highlights that AI's memory features can alleviate repeated disclosures, but raise concerns about privacy and the contextual integrity of shared information.

This research reveals critical insights into user behavior and privacy perceptions regarding sensitive personal information shared with AI. Understanding the nuanced ways individuals disclose disability, and their concerns about data handling, is vital for developing ethical AI systems and data governance frameworks. It underscores the need for strategies that balance utility with privacy and trust.

Disabled individuals often disclose their disability to conversational AI systems based on need, translating it into task-scoped instructions rather than direct identification.

Forward consideration, not a verified fact.