PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences

2025-07-29

AI Summary

To address the limited explainability of public health and biomedical AI systems—and their poor adaptability to diverse stakeholders (e.g., clinicians, policymakers, and the general public)—this paper proposes a structured, context-aware explainability framework. Methodologically, it innovatively integrates defeasible reasoning, adaptive natural language generation, and fine-grained user modeling to construct a multi-layered explanation architecture. This enables dynamic generation of audience-tailored argument chains, along with interactive simplification and contextual adaptation. Empirical validation across real-world scenarios—including medical term simplification, clinician–patient communication, and policy explanation—demonstrates significant improvements in explanation comprehensibility (+32.7%), credibility (+28.4%), and decision-support utility. The framework constitutes the first systematic XAI solution for health domains that simultaneously ensures logical rigor, expressive adaptability, and socio-technical acceptability.

Technology Category

• Humans and AI: Explainable AI (XAI) for Human Understanding
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Knowledge Representation and Reasoning: Argumentation

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• 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: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Enhancing transparency in AI-driven public health systems
• Providing adaptable explanations for diverse health stakeholders
• Improving interpretability and trust through structured argumentation

Innovation

• Structured argumentation for human-centered AI explanations
• Multi-layer architecture with defeasible reasoning and NLP
• Context-aware justifications personalized by user expertise