Explainable Artificial Intelligence: Leveraging Expert Systems and Neural Networks for Transparent Machine Learning Models
Keywords:
Explainable AI, Expert Systems, Neural Networks, Transparency, Hybrid Models, Interpretable Machine LearningAbstract
The rapid proliferation of artificial intelligence (AI) has highlighted the need for transparency, particularly in domains where interpretability is critical. Explainable Artificial Intelligence (XAI) seeks to address this challenge by combining symbolic reasoning from expert systems with the adaptive learning of neural networks. This paper explores hybrid models that integrate rule-based inference with deep learning architectures to improve transparency while maintaining predictive accuracy. We review existing literature, discuss the methodological strengths and weaknesses of current approaches, and propose a structured framework for explainable decision-making. The study concludes that a balanced synergy between expert systems and neural networks has the potential to reduce model opacity and enhance user trust.
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