Frameworks for Explainable Artificial Intelligence in High-Stakes Decision-Making Environments Such as Healthcare and Finance

Abstract

Explainable Artificial Intelligence (XAI) has become pivotal in high-stakes decision-making environments like healthcare and finance, where the interpretability of AI-driven decisions directly impacts human lives and economic stability. This paper explores various frameworks for implementing XAI in these critical domains, emphasizing their applicability, strengths, and limitations. It examines how transparency, fairness, and accountability can be achieved through model-agnostic and model-specific approaches, such as SHAP, LIME, and counterfactual reasoning. Moreover, the discussion highlights challenges, including balancing model performance with interpretability and addressing domain-specific nuances. This review consolidates existing knowledge and provides guidance for future research to enhance the trustworthiness and efficacy of AI systems in high-stakes applications.