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This chapter explores the transformative potential of Large Language Models (LLMs) across various critical sectors, including healthcare, finance, education, and autonomous systems. While LLMs offer unprecedented capabilities in data analysis, prediction, and automation, their increasing use in high-stakes domains brings forth critical concerns around accountability, fairness, and transparency. The necessity of Explainable Artificial Intelligence (XAI) is emphasized to address the “black box” nature of LLMs, ensuring that their decisions are understandable and trustworthy for professionals, regulators, and end-users. The chapter discusses the application of XAI techniques like SHAP, LIME, and counterfactual reasoning to enhance the interpretability of LLMs in these domains. It also examines the unique challenges and ethical considerations in each sector, advocating for ongoing research and collaboration to develop transparent, fair, and reliable AI systems.

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LLMs and XAI: Use Cases, Dependency and Challenges

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  • 发表日期：

    2026年

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  • Vishnu S. Pendyala,
  • Kargi Chauhan

摘要

This chapter explores the transformative potential of Large Language Models (LLMs) across various critical sectors, including healthcare, finance, education, and autonomous systems. While LLMs offer unprecedented capabilities in data analysis, prediction, and automation, their increasing use in high-stakes domains brings forth critical concerns around accountability, fairness, and transparency. The necessity of Explainable Artificial Intelligence (XAI) is emphasized to address the “black box” nature of LLMs, ensuring that their decisions are understandable and trustworthy for professionals, regulators, and end-users. The chapter discusses the application of XAI techniques like SHAP, LIME, and counterfactual reasoning to enhance the interpretability of LLMs in these domains. It also examines the unique challenges and ethical considerations in each sector, advocating for ongoing research and collaboration to develop transparent, fair, and reliable AI systems.