"It's a conversation, not a quiz": A Risk Taxonomy and Reflection Tool for LLM Adoption in Public Health

2024-11-04

arXiv.org

AI Summary

A systematic methodology for assessing the potential harms of large language models (LLMs) in high-stakes public health contexts—such as infectious disease control, opioid misuse mitigation, and intimate partner violence response—remains critically absent. Method: Drawing on qualitative insights from focus groups with public health practitioners and lived-experience stakeholders, this study employs thematic analysis and participatory knowledge co-creation to develop, for the first time, a four-dimensional LLM risk taxonomy tailored to public health: individual behavioral impact, human-centered care integrity, information ecosystem stability, and technical accountability—all grounded in real-world scenarios. Contribution/Results: The study introduces a novel reflective questioning toolkit to foster interdisciplinary risk governance. It delivers a consensus-based terminology framework, an actionable assessment pathway, and decision-support instruments—enabling responsible, context-sensitive, and non-replacement-oriented deployment of LLMs in public health practice.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as accessible information sources or communication tools across different domains. In public health -- where stakes are high and impacts extend across populations -- adopting LLMs poses unique challenges that require thorough evaluation. However, structured approaches for assessing potential risks in public health remain under-explored. To address this gap, we conducted focus groups with health professionals and health issue experiencers to unpack their concerns, situated across three distinct and critical public health issues that demand high-quality information: vaccines, opioid use disorder, and intimate partner violence. We synthesize participants' perspectives into a risk taxonomy, distinguishing and contextualizing the potential harms LLMs may introduce when positioned alongside traditional health communication. This taxonomy highlights four dimensions of risk in individual behaviors, human-centered care, information ecosystem, and technology accountability. For each dimension, we discuss specific risks and example reflection questions to help practitioners adopt a risk-reflexive approach. This work offers a shared vocabulary and reflection tool for experts in both computing and public health to collaboratively anticipate, evaluate, and mitigate risks in deciding when to employ LLM capabilities (or not) and how to mitigate harm when they are used.

Problem

• Assessing risks of LLM adoption in public health
• Developing a risk taxonomy for LLM-related harms
• Enhancing risk evaluation with domain expertise

Innovation

• Developed risk taxonomy for LLM adoption
• Conducted focus groups with professionals
• Created reflection tool for risk mitigation