PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences
This addresses the need for transparent and trustworthy AI explanations for diverse stakeholders in public health, though it appears incremental as it builds on existing explainable AI methods with a focus on argumentation.
The paper tackles the lack of structured and adaptable explanations in AI for public health and biomedical sciences by introducing PHAX, a framework that uses structured argumentation to generate context-aware, audience-specific justifications, demonstrated through use cases like medical term simplification and patient-clinician communication.
Ensuring transparency and trust in AI-driven public health and biomedical sciences systems requires more than accurate predictions-it demands explanations that are clear, contextual, and socially accountable. While explainable AI (XAI) has advanced in areas like feature attribution and model interpretability, most methods still lack the structure and adaptability needed for diverse health stakeholders, including clinicians, policymakers, and the general public. We introduce PHAX-a Public Health Argumentation and eXplainability framework-that leverages structured argumentation to generate human-centered explanations for AI outputs. PHAX is a multi-layer architecture combining defeasible reasoning, adaptive natural language techniques, and user modeling to produce context-aware, audience-specific justifications. More specifically, we show how argumentation enhances explainability by supporting AI-driven decision-making, justifying recommendations, and enabling interactive dialogues across user types. We demonstrate the applicability of PHAX through use cases such as medical term simplification, patient-clinician communication, and policy justification. In particular, we show how simplification decisions can be modeled as argument chains and personalized based on user expertise-enhancing both interpretability and trust. By aligning formal reasoning methods with communicative demands, PHAX contributes to a broader vision of transparent, human-centered AI in public health.