Explainable Artificial Intelligence Models for Decision Support in High-Risk Data Science Applications
Keywords:
Explainable AI, Decision Support, High-Risk Applications, Interpretability, Transparenc, Model ExplanationAbstract
The increasing reliance on artificial intelligence (AI) in high-risk domains such as healthcare, finance, criminal justice, and autonomous systems demands robust explainability to ensure accountability, transparency, and trust. Explainable AI (XAI) seeks to bridge the gap between model performance and human interpretability, enabling decision-makers to understand, trust, and appropriately act on model outputs. This paper investigates current explainable models, their integration into decision-making processes in high-stakes environments, and their limitations. We review key literature, compare interpretable models with black-box counterparts using case-based analysis, and offer guidance for responsible implementation. Two tables and visual aids provide a comparative overview and usage insights.
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