A practical review of explainable AI examines how transparency and interpretability improve trust in high-stakes applications. By introducing explainability frameworks, privacy-preserving methods, and human-centered evaluation principles, the study advances more accountable, secure, and trustworthy AI systems for real-world decision-making.
-- As artificial intelligence systems take on decisions in security, finance, and healthcare, the question of how to explain their reasoning has moved to the center of the field. In the research paper From Black Box to Glass Box: A Practical Review of Explainable Artificial Intelligence (XAI), the explainability of machine learning systems is examined as a practical requirement for high-stakes use rather than a purely technical or philosophical concern.
At the heart of that concern is a familiar problem: complex models often behave as "black boxes" whose decisions are difficult to follow, which erodes trust and accountability when those decisions carry real consequences. The research approaches this through two related ideas: transparency, the degree to which stakeholders can see how a system processes data, and interpretability, how well people can grasp the meaning of its predictions. It argues that transparency alone does not settle the matter, because a model can expose its inner workings and still remain opaque when the relationships it has learned grow too complex for people to follow.
Building on that distinction, the work introduces two ideas of its own, drawn from economics: marginal transparency and marginal interpretability. Both describe a pattern of diminishing returns, in which the first disclosures about a model, such as its structure or the features that matter most, yield the largest gains in understanding, while later and more technical additions contribute progressively less. In this view, explainability becomes a resource to be allocated with care rather than a property a system simply has or lacks.
From these principles, the research turns to the methods now used to make models interpretable, sorting them into two broad families. On one side are model-agnostic techniques such as LIME and SHAP, which explain individual predictions by approximating a model's local behavior or assigning each feature a share of the outcome; on the other are model-specific approaches such as decision trees and interpretable "glass-box" neural networks, which are built to be understandable from the start. The same lens is extended to large language models, whose scale and complexity place new demands on any attempt at explanation.
Throughout, the study keeps its focus on security and privacy. It treats privacy-preserving explainability as a concern in its own right, observing that explanations can themselves reveal sensitive information, and it ties interpretability to applications such as intrusion detection, malware analysis, and fraud detection, as well as to the "right to explanation" set out in the European Union's General Data Protection Regulation. From there, it looks toward the years through 2030, outlining a direction in which explanations are measured against shared standards, integrated into Zero Trust security architectures, adapted to the expertise of different users, and gradually built into systems able to account for themselves.
Among the lead authors of the research is Xiaoming Liu, who is credited with its methodology, investigation, data curation, and original draft. Beyond this published work, Liu, who holds a master's in computer engineering from San Jose State University, is a software engineer specializing in large-scale privacy engineering, AI-driven compliance systems, and data governance for global technology platforms. That work has centered on the privacy-monitoring and compliance infrastructure behind requirements such as the U.S. Federal Trade Commission's privacy frameworks and the European Union's General Data Protection Regulation, where the task is to enable continuous monitoring, automated risk detection, and the collection of compliance evidence across distributed systems.
The same concerns now carry into his research on generative AI, an in-development effort he describes as an AI-Enhanced Data Quality and Trustworthiness Framework for Large Language Models, which uses automated evaluation to catch low-quality, inconsistent, or potentially harmful data before it enters model training, to reduce hallucinations and strengthen alignment. Read alongside the review, that direction points beyond any single method, toward a setting in which explainability becomes part of what makes AI systems trustworthy in the domains where security, privacy, and accountability are most at stake.
Contact Info:
Name: Xiaoming Liu
Email: Send Email
Organization: Xiaoming Liu
Website: https://scholar.google.co.uk/citations?hl=en&authuser=1&user=6m0sDXwAAAAJ
Release ID: 89196351
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