Comparative Study of Explainable AI Techniques for Enhancing Model Interpretability
Abstract
As AI models grow in complexity, their interpretability becomes crucial for trust and accountability. This paper presents a comparative analysis of explainable AI (XAI) techniques, including SHAP, LIME, and counterfactual explanations, across different machine learning models. We evaluate these methods in terms of transparency, computational efficiency, and usability in high-stakes domains such as healthcare and finance. The results provide insights into selecting the most effective XAI approach for balancing accuracy and interpretability in real-world AI applications.
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