@inproceedings{zhao-etal-2026-telling,
title = "Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare {AI}",
author = "Zhao, Xingmeng and
Wang, Tongnian and
Schumacher, Dan and
Rammouz, Veronica and
Rios, Anthony",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.413/",
pages = "8476--8516",
ISBN = "979-8-89176-395-1",
abstract = "Artificial intelligence (AI) is rapidly transforming healthcare, enabling the fast development of tools such as stress monitors, wellness trackers, and mental health chatbots. However, this rapid and low-barrier development can also introduce risks, including bias, privacy violations, and unequal access, especially when systems overlook real-world contexts, diverse user needs, and cultural settings. Many recent approaches use AI to identify such risks automatically, but this can reduce human engagement in understanding how harms arise, who they affect, and which stakeholder needs remain unspoken. We present a human-centered ethical foresight framework that generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment. In a user study, participants who engaged with stories identified a broader range of harms, distributing their responses more evenly across all 17 harm types, whereas those who did not engage with stories focused primarily on privacy and well-being (79.1{\%}). Overall, our findings suggest that storytelling helps people anticipate potential risks and benefits and reflect more broadly on how AI systems may affect different users, contexts, and often unspoken needs."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhao-etal-2026-telling">
<titleInfo>
<title>Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI</title>
</titleInfo>
<name type="personal">
<namePart type="given">Xingmeng</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tongnian</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Dan</namePart>
<namePart type="family">Schumacher</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronica</namePart>
<namePart type="family">Rammouz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anthony</namePart>
<namePart type="family">Rios</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-07</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: ACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Maria</namePart>
<namePart type="family">Liakata</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Viviane</namePart>
<namePart type="given">P</namePart>
<namePart type="family">Moreira</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jiajun</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">David</namePart>
<namePart type="family">Jurgens</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">San Diego, California, United States</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-395-1</identifier>
</relatedItem>
<abstract>Artificial intelligence (AI) is rapidly transforming healthcare, enabling the fast development of tools such as stress monitors, wellness trackers, and mental health chatbots. However, this rapid and low-barrier development can also introduce risks, including bias, privacy violations, and unequal access, especially when systems overlook real-world contexts, diverse user needs, and cultural settings. Many recent approaches use AI to identify such risks automatically, but this can reduce human engagement in understanding how harms arise, who they affect, and which stakeholder needs remain unspoken. We present a human-centered ethical foresight framework that generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment. In a user study, participants who engaged with stories identified a broader range of harms, distributing their responses more evenly across all 17 harm types, whereas those who did not engage with stories focused primarily on privacy and well-being (79.1%). Overall, our findings suggest that storytelling helps people anticipate potential risks and benefits and reflect more broadly on how AI systems may affect different users, contexts, and often unspoken needs.</abstract>
<identifier type="citekey">zhao-etal-2026-telling</identifier>
<location>
<url>https://aclanthology.org/2026.findings-acl.413/</url>
</location>
<part>
<date>2026-07</date>
<extent unit="page">
<start>8476</start>
<end>8516</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI
%A Zhao, Xingmeng
%A Wang, Tongnian
%A Schumacher, Dan
%A Rammouz, Veronica
%A Rios, Anthony
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhao-etal-2026-telling
%X Artificial intelligence (AI) is rapidly transforming healthcare, enabling the fast development of tools such as stress monitors, wellness trackers, and mental health chatbots. However, this rapid and low-barrier development can also introduce risks, including bias, privacy violations, and unequal access, especially when systems overlook real-world contexts, diverse user needs, and cultural settings. Many recent approaches use AI to identify such risks automatically, but this can reduce human engagement in understanding how harms arise, who they affect, and which stakeholder needs remain unspoken. We present a human-centered ethical foresight framework that generates speculative user stories and supports multi-agent discussions to help people reflect on potential benefits and harms of healthcare AI before deployment. In a user study, participants who engaged with stories identified a broader range of harms, distributing their responses more evenly across all 17 harm types, whereas those who did not engage with stories focused primarily on privacy and well-being (79.1%). Overall, our findings suggest that storytelling helps people anticipate potential risks and benefits and reflect more broadly on how AI systems may affect different users, contexts, and often unspoken needs.
%U https://aclanthology.org/2026.findings-acl.413/
%P 8476-8516
Markdown (Informal)
[Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI](https://aclanthology.org/2026.findings-acl.413/) (Zhao et al., Findings 2026)
ACL