Xiaoming Liu Highlights the Role of Explainable AI in Building Trustworthy and Privacy-Preserving Systems
Publish Date: 2026-06-30 04:28:00
Source Domain: macaubusiness.com
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…
