Explainability in Machine Learning: Bridging the Gap Between Model Complexity and Interpretability

Abstract

As machine learning models become increasingly sophisticated, the need for understanding and interpreting their decisions becomes paramount, especially in high-stakes applications such as healthcare, finance, and criminal justice. This paper addresses the challenge of balancing model complexity with interpretability, aiming to provide insights into the decision-making processes of complex models. The first section of the paper reviews the current landscape of machine learning models, highlighting the trade-off between model complexity and interpretability. It discusses the rise of complex models such as deep neural networks and ensemble methods, which often achieve state-of-the-art performance but lack transparency in their decision-making mechanisms. Next, the paper explores the importance of model interpretability in various real-world scenarios, emphasizing the ethical, legal, and social implications of black-box models. The significance of model explainability in gaining user trust, ensuring accountability, and facilitating model deployment in sensitive domains is discussed.