PHAX: A Structured Argumentation Framework for User-Centered Explainable AI in Public Health and Biomedical Sciences
2025-07-30
Summary
The article introduces PHAX, a framework designed to improve the transparency and trustworthiness of AI in public health and biomedical sciences by providing user-centered explanations. PHAX combines structured argumentation, natural language processing, and user modeling to create context-aware explanations tailored to different stakeholders, such as clinicians, policymakers, and the general public. The framework is demonstrated through use cases like simplifying medical terms and supporting patient-clinician communication.
Why This Matters
The need for transparent and explainable AI is critical in public health and biomedical sciences due to the high-stakes nature of decisions impacting patient care and public health policies. Traditional AI models often lack the ability to provide user-specific explanations, which can undermine trust and accountability. By offering structured, audience-specific justifications, PHAX addresses these limitations and enhances the clarity and comprehensibility of AI-driven decisions.
How You Can Use This Info
Professionals in healthcare and public health policy can leverage PHAX to improve communication and decision-making processes by providing explanations that are not only accurate but also tailored to the audience's understanding and needs. This can enhance trust and engagement with AI systems, leading to better outcomes in patient care and policy implementation. Additionally, integrating PHAX into existing systems could facilitate more transparent and interactive dialogues with stakeholders, improving overall acceptance and effectiveness of AI applications in these fields.