Telling Speculative Stories to Help Humans Imagine the Harms of Healthcare AI
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 42 of its 102 references that are in the map. First posted on arXiv 16 Oct 2025.
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42 of 102 references.
References · 42
- 2024The Llama 3 Herd of Modelscited by 14,192
- 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
- 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,068
- 2018Model Cards for Model Reportingcited by 1,068
- 2024From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipelinecited by 484
- 2025Agentic Context Engineering: Evolving Contexts for Self-Improving Language Modelscited by 349
- 2023Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaborationcited by 246
- 2018Plan-And-Write: Towards Better Automatic Storytellingcited by 232
- 2022Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionalscited by 191
- 2020Human-Centered Artificial Intelligence: Reliable, Safe & Trustworthycited by 182
- 2024PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Actioncited by 161
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
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- 2022Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reductioncited by 137
- 2019Strategies for Structuring Story Generationcited by 124
- 2025Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Taskcited by 103
- 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
- 2022Interactive Model Cards: A Human-Centered Approach to Model Documentationcited by 61
- 2024Agents' Room: Narrative Generation through Multi-step Collaborationcited by 57
- 2025Embodied AI Agents: Modeling the Worldcited by 57
- 2023AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applicationscited by 53
- 2024IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generationcited by 35
- 2020Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directionscited by 34
- 2023Assessing Language Model Deployment with Risk Cardscited by 30
- 2023AHA!: Facilitating AI Impact Assessment by Generating Examples of Harmscited by 26
- 2023ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Gamescited by 22
- 2024Creating Suspenseful Stories: Iterative Planning with Large Language Modelscited by 22
- 2024HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playingcited by 22
- 2024Making Large Language Models into World Models with Precondition and Effect Knowledgecited by 21
- 2022Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceabilitycited by 14
- 2024Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversationscited by 13
- 2025Mapping Caregiver Needs to AI Chatbot Design: Strengths and Gaps in Mental Health Support for Alzheimer's and Dementia Caregiverscited by 13
- 2024ExploreGen: Large Language Models for Envisioning the Uses and Risks of AI Technologiescited by 12
- 2025BookWorld: From Novels to Interactive Agent Societies for Creative Story Generationcited by 12
- 2025AI Mismatches: Identifying Potential Algorithmic Harms Before AI Developmentcited by 10
- 2024Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefitscited by 9
- 2025RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reportingcited by 9
- 2025STORYTELLER: An Enhanced Plot-Planning Framework for Coherent and Cohesive Story Generationcited by 9
- 2025SurveyG: A Multi-Agent LLM Framework with Hierarchical Citation Graph for Automated Survey Generationcited by 7
- 2023Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)cited by 6
- 2025Avoidance Decoding for Diverse Multi-Branch Story Generationcited by 6
- 2024Supporting Industry Computing Researchers in Assessing, Articulating, and Addressing the Potential Negative Societal Impact of Their Workcited by 4
- 2025Walk in Their Shoes to Navigate Your Own Path: Learning About Procrastination Through A Serious Gamecited by 1
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