From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
This addresses the need for AI systems that enhance human comprehension in sociotechnical contexts like healthcare, representing a shift from algorithmic transparency to user-centered explanations.
The paper tackles the problem that current explainable AI (XAI) approaches often fail to support meaningful human understanding by introducing 'Explanatory AI' as a new paradigm that uses generative AI to provide context-sensitive, multimodal explanations. Empirical validation with healthcare professionals shows users consistently prefer these explanations over technical transparency.
Current explainable AI (XAI) approaches prioritize algorithmic transparency and present explanations in abstract, non-adaptive formats that often fail to support meaningful end-user understanding. This paper introduces "Explanatory AI" as a complementary paradigm that leverages generative AI capabilities to serve as explanatory partners for human understanding rather than providers of algorithmic transparency. While XAI reveals algorithmic decision processes for model validation, Explanatory AI addresses contextual reasoning to support human decision-making in sociotechnical contexts. We develop a definition and systematic eight-dimensional conceptual model distinguishing Explanatory AI through narrative communication, adaptive personalization, and progressive disclosure principles. Empirical validation through Rapid Contextual Design methodology with healthcare professionals demonstrates that users consistently prefer context-sensitive, multimodal explanations over technical transparency. Our findings reveal the practical urgency for AI systems designed for human comprehension rather than algorithmic introspection, establishing a comprehensive research agenda for advancing user-centered AI explanation approaches across diverse domains and cultural contexts.