As Artificial Intelligence (AI) systems increasingly mediate critical decisions in healthcare, finance, and governance, the opacity of complex models—often termed the "black box" problem—poses a significant barrier to trust and regulatory compliance. This article addresses the urgent need for Explainable AI (XAI) by proposing a novel theoretical framework that integrates Granular Computing, specifically Multi-Granularity Decision-Theoretic Rough Sets, with Hesitant Fuzzy Linguistic theory. Drawing upon Zadeh’s foundational concept of information granulation, we argue that human reasoning is inherently granular and fuzzy, rather than binary. Therefore, AI systems designed to interact with human stakeholders must adopt a "Three-Way Decision" methodology (Accept, Reject, Defer) to accurately reflect the ambiguity of real-world data. The study evaluates this framework against current Deep Learning applications in neurodegenerative disease diagnosis (Alzheimer’s and Parkinson’s) and epidemiological modeling (COVID-19). Results indicate that while "crisp" numerical models may achieve marginal gains in raw accuracy, granular fuzzy models offer superior interpretability, allowing clinicians to understand the "boundary regions" of a diagnosis. Furthermore, the article provides an extensive analysis of the legal and ethical landscapes, specifically examining the European Union’s GDPR Recital 58 and the requirement for "Transparency by Design." We conclude that integrating multi-granularity fuzzy models into high-stakes AI pipelines is not merely a technical optimization but an ethical imperative to ensure algorithmic accountability and align machine learning outputs with human cognitive processes.

Explainable AI (XAI), Granular Computing, Fuzzy Logic, Three-Way Decisions

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