The Future of Explainable AI: Balancing Accuracy and Interpretability
Abstract
As AI systems become more complex, the need for transparency and explainability in decision-making grows. This paper reviews the latest advancements in Explainable AI (XAI), examining techniques such as SHAP values, LIME, and counterfactual explanations. We propose a novel hybrid model that balances model accuracy with interpretability, ensuring AI decisions are understandable to human users. Case studies in healthcare, finance, and legal AI applications illustrate the trade-offs between performance and explainability. Ethical implications, regulatory considerations, and future research directions for improving AI transparency are also discussed.
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