Interpretability and Explainability in Machine Learning Systems

Abstract

Interpretability and explainability are critical aspects of machine learning (ML) systems, especially when deployed in high-stakes domains. This survey reviews key definitions, techniques, and challenges associated with interpretability and explainability in ML. We categorize approaches into inherently interpretable models, model-specific explanations, and model-agnostic post-hoc methods. The paper discusses trade-offs between model performance and transparency, evaluation metrics, and emerging directions to enhance user trust and regulatory compliance.