Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI

ACL ARR 2026 January Submission8154 Authors

Keywords: Human-centered AI, AI ethics, speculative storytelling, story generation, healthcare AI

Abstract: 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 17 harm types. In contrast, those who did not read stories focused primarily on privacy and well-being (79.1\%). 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.

Paper Type: Long

Research Area: Human-AI Interaction/Cooperation and Human-Centric NLP

Research Area Keywords: human-AI interaction/cooperation, human-in-the-loop, human-centered evaluation, user-centered design, ethical considerations in NLP applications

Contribution Types: NLP engineering experiment, Data analysis

Languages Studied: English

Submission Number: 8154