Enhancing AI Decision-Making with Explainable Large Language Models (LLMs) in Critical Applications

Abstract

LLMs are being integrated into critical environments, such as healthcare, finance, and autonomous systems, which raise serious transparency, trust, and accountability concerns. Despite LLMs showing impressive capabilities with decision-making, the "black-box" nature of LLMs frequently limits LLM usage in high-stakes environments. Explainable AI (XAI) approaches seek to fill this gap to facilitate better understandability of decisions made by an LLM, which is an important aspect for end user trust, verification of compliance with regulations. Attention Mechanisms, Post-Hoc Explanations, Rule-based Reasons, etc. We examine existing problems, present a foundation for explainable LLM decision-making, and consider the implications of these approaches for deployment in the real world. Our research highlights the need to balance performance of model output with transparency for ethical and effective AI implementation across important industries.

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