Explainable AI: Enhancing Transparency and Trust in Machine Learning

Abstract

Artificial Intelligence (AI) has become widely used for solving complex problems across various domains. However, many advanced AI models, especially deep learning systems, operate as "black boxes," where the decision-making process is not transparent. This lack of interpretability raises concerns about trust, accountability, and ethical use, particularly in critical areas such as healthcare and finance. Explainable Artificial Intelligence (XAI) addresses these challenges by making AI models more transparent and understandable. This paper presents a study of XAI techniques, focusing on methods such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (Shapley Additive explanations). These techniques help explain how input features influence predictions, providing both local and global interpretability. The methodology involves training a machine learning model and applying XAI techniques to analyse its predictions. The results show that LIME provides simple local explanations, while SHAP offers more consistent and detailed insights into feature importance. These approaches improve trust, transparency, and help identify potential biases in AI systems. Despite its advantages, XAI faces challenges such as computational cost and trade-offs between accuracy and interpretability. Overall, XAI is essential for developing reliable, ethical, and human-centered AI systems.

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