Speculative Model Risk in Healthcare AI: Using Storytelling to Surface Unintended Harms
This addresses the problem of mitigating risks like bias and unequal access in healthcare AI for developers and users, though it is incremental as it builds on existing human-centered approaches.
The paper tackles the problem of unintended harms in healthcare AI by proposing a human-centered framework using storytelling and multi-agent discussions to help people think creatively about potential risks before deployment. In a user study, participants who read stories recognized a broader range of harms, distributing responses more evenly across 13 harm types, compared to those without stories who focused 58.3% on privacy and well-being.
Artificial intelligence (AI) is rapidly transforming healthcare, enabling fast development of tools like stress monitors, wellness trackers, and mental health chatbots. However, rapid and low-barrier development can introduce risks of bias, privacy violations, and unequal access, especially when systems ignore real-world contexts and diverse user needs. Many recent methods use AI to detect risks automatically, but this can reduce human engagement in understanding how harms arise and who they affect. We present a human-centered framework that generates user stories and supports multi-agent discussions to help people think creatively about potential benefits and harms before deployment. In a user study, participants who read stories recognized a broader range of harms, distributing their responses more evenly across all 13 harm types. In contrast, those who did not read stories focused primarily on privacy and well-being (58.3%). Our findings show that storytelling helped participants speculate about a broader range of harms and benefits and think more creatively about AI's impact on users.