Abstract
This research paper explores self-explaining AI models that bridge the gap between complex black-box algorithms and human interpretability. The study focuses on techniques like LIME, SHAP, attention mechanisms, and rule-based systems to create locally interpretable models. By providing transparent and understandable explanations for AI predictions, these models enhance user trust and comprehension. Real-world applications in healthcare, finance, and autonomous systems are evaluated to demonstrate the effectiveness of self-explaining AI models. Ethical considerations regarding fairness, bias, and accountability in AI decision-making are also addressed. The findings underscore the potential of such models to unlock the mysteries of complex algorithms, making AI more accessible and interpretable for diverse applications.
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