Explainability in AI: Interpretable Models for Data Science
Ali Mohd Ali · International Journal for Research in Applied Science and Engineering Technology · 2025
As artificial intelligence (AI) continues to drive advancements across various domains, the need for explainability in AI models has become increasingly critical. Many state-of-the-art machine learning models, particularly deep learning architectures, operate as "black boxes," making their decision-making processes difficult to interpret. Explainable AI (XAI) aims to enhance model transparency, ensuring that AI-driven decisions are understandable, trustworthy, and aligned with ethical and regulatory standards. This paper explores different approaches to AI interpretability, including intrinsically interpretable models such as decision trees and logistic regression, as well as post-hoc methods like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations). Additionally, we discuss the challenges of explainability, including the trade-off between accuracy and interpretability, scalability issues, and domain-specific requirements. The paper also highlights real-world applications of XAI in healthcare, finance, and autonomous systems. Finally, we examine future research directions, emphasizing hybrid models, causal explainability, and human-AI collaboration. By fostering more interpretable AI systems, we can enhance trust, fairness, and accountability in data science applications.