In a research paper titled *From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI)*, Xiaoming Liu examines how transparency and interpretability improve trust in high-stakes applications such as security, finance, and healthcare. The study addresses the problem of complex models behaving as "black boxes" whose decisions are difficult to follow, eroding trust and accountability. It introduces two ideas from economics—marginal transparency and marginal interpretability—describing diminishing returns where initial disclosures yield the largest gains in understanding. The research sorts interpretability methods into model-agnostic techniques like LIME and SHAP, and model-specific approaches such as decision trees and interpretable "glass-box" neural networks. It extends this lens to large language models and treats privacy-preserving explainability as a concern, noting explanations can reveal sensitive information. The study ties interpretability to applications including intrusion detection, malware analysis, and fraud detection. The paper was released from New York, NY, United States, on June 29, 2026.