Explainable Artificial Intelligence Models for Interpretable Decision-Making in High-Stakes Applications

Abstract

Explainable Artificial Intelligence (XAI) has been taken into higher stakes by the applications of high-stakes performances including healthcare diagnosis, financial fraud detection, cybersecurity analytics, legal decision support, and self-driven intelligent systems, where a transparent and trustworthy decision-making process is essential. Nevertheless, most traditional machine learning and deep learning systems are black-box systems, which can be extremely accurate in their predictions, but are not interpretable and are not based on human-irable arguments. This weakness lowers the user trust, responsibility, and reliability in real-world scenarios that are sensitive, and wrong or unaccount-explained decisions can cause dire outcomes. To overcome this challenge, this paper suggests a next generation Explainable Artificial Intelligence framework to interpret decisions made in high stakes applications. The proposed framework combines machine learning and deep learning architectures with several explainability mechanisms such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), attention-based feature visualization, and counterfactual reasoning to produce both global and local model explanations. It has a hybrid decision interpretation module that enhances the analysis of feature attribution, consistency of explanations and semantic transparency of model predictions. The model was tested on benchmark high stakes datasets in real time intelligent decision making scenarios. Experimental findings indicate that the proposed method realized a 96.4 percent prediction accuracy and a 95.1 percent precision as well as a 94.7 and 94.9 percent recall, F1-score, and AUC-ROC, respectively, outperforming traditional black-box machine learning and deep learning models and significantly enhancing interpretability, transparency, and human-understandable reasoning with low computational loads that can be effectively applied in critical applications.