Explainable AI Frameworks for Large Language Models in High-Stakes Decision-Making

In high-stakes decision-making, such as healthcare diagnostics, financial forecasting, and legal judgments, trust and transparency are key in AI systems. LLMs have indeed proved to be very capable in processing and generating human-like text, making them invaluable tools in these critical sectors. However, the opacity of their decision-making processes poses a significant challenge for accountability and ethical compliance. This paper introduces comprehensive Explainable AI frameworks tailored for LLMs with the goal of improving their interpretability and reliability for high-stakes environments. Using publicly available real-world datasets, we systematically study the effectiveness of various XAI techniques, including attention visualization, feature importance mapping, and counterfactual explanations. Our findings point to the fact that such embedding serves not only to demystify the decision pathways of LLMs but also to bring their operations in line with regulatory standards and stakeholder expectations. This research makes its contribution to responsible LLM deployment in situations where mistakes have significant consequences by closing the gap between sophisticated AI capabilities and the need for transparent decision-making. The proposed XAI frameworks will, in the end, be a starting point for trust, ethics, and wider acceptance of AI-driven solutions in high-stakes domains.