EXPLAINABLE ARTIFICIAL INTELLIGENT MODEL FOR ACCURATE PREDICTIVE LEARNING

Explainable Artificial Intelligence (XAI) has emerged as a critical research domain aimed at enhancing the transparency, interpretability, and trustworthiness of complex machine learning models. While modern AI systems such as deep neural networks deliver high predictive accuracy, they often function as “black boxes,” making it difficult for users to understand how decisions are made. This limitation restricts their adoption in high-stakes domains such as healthcare, finance, autonomous systems, and industrial automation.
This paper proposes an Explainable Artificial Intelligent (XAI) model designed for accurate predictive learning by integrating interpretable machine learning techniques with high-performance predictive algorithms. The proposed model combines feature attribution methods, post-hoc explanation techniques, and intrinsic interpretability mechanisms to improve transparency without sacrificing accuracy. Furthermore, it introduces a hybrid architecture that balances model complexity and interpretability, ensuring both robust prediction performance and human-understandable explanations.
The study also evaluates the performance of the proposed model using standard datasets and compares it with traditional machine learning and deep learning models. Experimental results demonstrate that the XAI model achieves competitive accuracy while significantly improving interpretability and trustworthiness.