Treffer: The Importance of Explainable AI in the Era of Agentic Systems.

The article focuses on the importance of Explainable AI (XAI) in enhancing the transparency and accountability of agentic AI systems, which are capable of setting goals and making independent decisions. It discusses the necessity of understanding the decision-making processes of these systems to build trust, ensure safety, and facilitate accountability, especially in critical applications like healthcare. The article outlines various XAI methodologies, including intrinsic and post-hoc explainability techniques, and emphasizes the role of tools like SHAP (SHapley Additive exPlanations) in interpreting AI predictions, particularly in early sepsis detection. As agentic AI becomes more integrated into society, the need for effective explainability methods is highlighted as essential for ethical and responsible use. [Extracted from the article]

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