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

arXiv: https://arxiv.org/abs/2510.14718 · HTML: https://mlsanity.com/paper/2510.14718/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 42 of its 102 references that are in the map.
- First posted on arXiv 16 Oct 2025.

## Builds on (most cited first, 30 of 42)

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- [AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms](https://mlsanity.com/paper/2306.03280/) (2023)
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- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
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- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)

## Abstract (arXiv)

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.