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Recent coverage highlights that explainable AI (XAI) remains an unresolved challenge: even top AI executives admit they cannot always determine why a model arrives at a particular output, underscoring a lack of reliable transparency mechanisms. This gap matters because business leaders rely on XAI to build trust, meet audit requirements, and control AI behaviour. The current focus is on developing standards and tools that improve transparency and accountability, but a definitive "gold standard" for explainability has yet to emerge. [[1]]