Explainable Machine Learning: Methods, Applications, and Challenges

Abstract

As machine learning models are increasingly deployed in domains such as healthcare, finance, and public policy, the opacity of high-performing models has become a central obstacle to trust, accountability, and regulatory compliance. Explainable machine learning (often discussed under the broader umbrella of explainable artificial intelligence, or XAI) addresses this obstacle by developing techniques that make model behavior interpretable to humans, either by design or after the fact. This paper surveys the current landscape of explainable machine learning. It reviews the conceptual distinction between interpretability and explainability, examines the principal families of explanation methods including intrinsically interpretable models, post-hoc model-agnostic techniques such as SHAP and LIME, and counterfactual explanations, and discusses their extension to deep learning and large language models. It further reviews applications in healthcare, finance, and other high-stakes domains, summarizes the evolving regulatory landscape shaped by the EU AI Act and related frameworks, and outlines open challenges, including the performance-interpretability trade-off, the faithfulness and stability of explanations, and the risk of a false sense of security created by explanations that appear plausible but do not reflect a model's true reasoning. The paper concludes with directions for future research, including standardized evaluation protocols and human-centered explanation design.