A Transparent Explainable AI Model for High-Risk Domain Predictions Using Multi-Layer Interpretability
Abstract
This study introduces a transparent explainable AI model built to support predictions in high-risk domains such as healthcare, finance, and public safety. The architecture combines a deep neural network with an interpretability layer that generates multi-level explanations, including feature attribution maps, rule-based reasoning, and natural-language summaries. Experiments on multiple benchmark datasets show improved prediction reliability while maintaining interpretability without sacrificing accuracy. The proposed model provides a practical pathway for deploying trustworthy AI systems where accountability and clarity are essential.
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