Published Paper Details:

Explainable Artificial Intelligence (XAI) is a field of AI that focuses on making machine learning models transparent, interpretable, and understandable to humans. As AI systems become increasingly complex and integral to decision-making in areas like healthcare, finance, and autonomous systems, the need for interpretability grows to ensure trust, fairness, and accountability. XAI techniques aim to provide insights into model predictions, helping users understand the rationale behind AI-driven decisions. Methods such as feature importance analysis, SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), and decision trees enable a balance between model performance and interpretability. The adoption of XAI not only improves user trust but also ensures compliance with ethical and regulatory standards like GDPR. This paper explores various XAI techniques, their applications, challenges, and future directions in bridging the gap between AI's predictive power and human interpretability.