An Analytical Study of Explainable AI Models for High-Stakes Decision Systems

Abstract

Artificial Intelligence (AI) is becoming more and more common in critical decision-making areas like healthcare diagnosis, financial risk assessment, and criminal justice, where the stakes are incredibly high and the consequences can be serious and irreversible. Even though advanced machine learning models often boast impressive predictive accuracy, their black-box nature raises important issues around transparency, trust, fairness, and accountability. This has sparked a growing interest in Explainable Artificial Intelligence (XAI), which seeks to make AI-driven decisions clearer and more reliable for the people involved. In this paper, we dive into an analytical study of explainable AI models used in these high-stakes environments, focusing on finding the right balance between predictive performance and interpretability. We compare traditional black-box models with those that are inherently interpretable, as well as post-hoc explanation techniques. We take a closer look at popular XAI methods like Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to see how feature-level explanations can boost transparency without significantly sacrificing accuracy.

To back up our findings, we conduct experimental analysis using benchmark datasets that are commonly used in critical decision-making fields, including healthcare, finance, and criminal justice. We evaluate models like Logistic Regression, Random Forest, and Gradient Boosting using standard performance metrics alongside criteria focused on explainability. The results show that explainable frameworks not only enhance model transparency and user trust but also maintain competitive predictive performance. The study emphasizes how crucial explainability is when it comes to tackling ethical issues, spotting biases, and ensuring compliance with regulations in high-risk situations. In essence, the findings show that explainable AI is key to creating decision support systems that are trustworthy, accountable, and centered on human needs. This makes it absolutely vital for the responsible use of AI in high-stakes scenarios.