Explainable AI: Bridging the Gap between Algorithms and Interpretability
Abstract
The rapid adoption of machine learning and artificial intelligence (AI) models in various applications has highlighted the importance of understanding model decisions and making them interpretable. This paper explores the concept of Explainable AI (XAI) and its role in bridging the gap between complex algorithms and interpretability. We delve into various techniques and approaches that enhance the transparency and accountability of AI systems, making them more accessible to users and regulators. We discuss the significance of XAI in healthcare, finance, and autonomous systems and present case studies that demonstrate its practical utility. Additionally, we provide insights into the current challenges and future directions in XAI research.
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References
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