Quantifying the Trade-Offs Between Model Interpretability and Predictive Accuracy in Explainable Artificial Intelligence for High-Stakes Decision Systems
Keywords:
Explainable Artificial Intelligence (XAI), model interpretability, predictive accuracy, black-box models, high-stakes decisions, transparency, fairness, algorithmic accountabilityAbstract
In high-stakes domains such as healthcare, finance, and criminal justice, the adoption of artificial intelligence (AI) demands a careful balance between predictive accuracy and interpretability. This paper examines the inherent trade-offs between these two often-competing objectives in Explainable AI (XAI), highlighting the consequences of privileging one over the other in high-impact decision contexts. By analyzing existing interpretability methods, benchmarking model performance, and comparing real-world case studies, this work emphasizes the complexity of model selection for decision-critical applications. Our findings reveal that while interpretable models tend to underperform in predictive power relative to complex black-box models, recent developments in post-hoc and hybrid interpretability frameworks can help bridge the gap. We conclude by outlining a decision-theoretic framework for balancing interpretability and accuracy, emphasizing context-specific optimization strategies.
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