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TruaceTracing the truth around AIWednesday, August 5, 2026
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Science·P Space·Evidence-backed problem·Published 2026-07-22

A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities

The widespread adoption of Artificial Intelligence (AI) in critical domains, such as healthcare, finance, law, and autonomous systems, has brought unprecedented societal benefits. Its black-box (sub-symbolic) nature allows AI to compute prediction without explaining the rationale to the end user, resulting in lack of transparency between human and machine. Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures. To address this concern, explainability is of param…

TRV-2026-0489Peer-reviewedPermanent record — cite & verify
A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities

Bloom's Taxonomy for Course Design and Teaching by DrAntonT. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

This peer-reviewed survey from September 2025 provides a comprehensive overview of Explainable Artificial Intelligence, covering foundational concepts, terminology, taxonomy of methods and application domains including healthcare, finance, law and autonomous systems.

It matters because opacity of deep learning models undermines transparency, trust and accountability in high-stakes use, yet progress is constrained by definitional disagreement, accuracy-interpretability trade-offs and absence of standard metrics, leaving how to build human-centric, interactive and evaluable explanations unresolved.

Main points
  • Survey defines foundational concepts, definitions, terminologies and taxonomy of XAI methods.
  • Application domains reviewed include healthcare, finance, law and autonomous systems where opacity raises concerns.
  • Core challenges identified are no universal definition, accuracy-interpretability trade-off and lack of standardized evaluation metrics.
Problem

Black-box sub-symbolic AI, particularly deep learning, computes predictions without explaining rationale, creating opacity and lack of transparency for end users.

The rundown

The source is a peer-reviewed survey published 2025-09-03 that synthesizes XAI literature. It outlines definitions and a taxonomy of methods and reviews use in healthcare, finance, law and autonomous systems.

It frames explainability as needed for transparency, trust and accountability, then details open problems and points to future work on human-centric design, interactive explanation and standardized evaluation frameworks.

Sources

Reader signal

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The debate