# Xiaoming Liu Highlights the Role of Explainable AI in Building Trustworthy and Privacy-Preserving Systems

- Link: https://www.thailand-business-news.com/pr-news/xiaoming-liu-highlights-the-role-of-explainable-ai-in-building-trustworthy-and-privacy-preserving-systems
- Published: 2026-06-29T22:47:00+07:00
- Author: MarketersMEDIA Newswire

_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._

New York, NY, United States, June 29, 2026 — 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 LiuEmail: [Send Email](https://send.releasecontact.com/89196351)
Organization: Xiaoming LiuWebsite: [https://scholar.google.co.uk/citations?hl=en&authuser=1&user=6m0sDXwAAAAJ](https://scholar.google.co.uk/citations?hl=en&authuser=1&user=6m0sDXwAAAAJ)

Release ID: 89196351

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**Source** : [Xiaoming Liu Highlights the Role of Explainable AI in Building Trustworthy and Privacy-Preserving Systems](https://news.marketersmedia.com/xiaoming-liu-highlights-the-role-of-explainable-ai-in-building-trustworthy-and-privacy-preserving-systems/89196351?rand=206000)

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