Defending and Explaining the AI Black Box

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Defending and Explaining the AI Black Box

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These sources explore the evolving landscape of Explainable AI (XAI) and the practical frameworks used to maintain human oversight in automated systems. One source distinguishes between human-in-the-loop, where people must approve actions before execution, and human-on-the-loop, which involves retrospective monitoring of autonomous processes. The other source provides a comprehensive survey on using Large Language Models (LLMs) to translate complex "black box" algorithms into understandable natural language narratives. Together, they address critical architectural tradeoffs regarding latency, risk, and transparency across high-stakes industries like healthcare and finance. By examining various interpretability techniques and oversight patterns, the texts illustrate how to build trust and ensure ethical accountability in artificial intelligence. Ultimately, the materials emphasize that combining automated reasoning with human judgment is essential for creating reliable, user-centric AI workflows.

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