Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0489
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-22T03:59:05.157603Z
status: published
lens: p_space
sector: science
headline: A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities
dek: 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…
gain_title: (none)
problem_title: Black-box sub-symbolic AI, particularly deep learning, computes predictions without explaining rationale, creating opacity and lack of transparency for end users.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Black-box sub-symbolic AI, particularly deep learning, computes predictions without explaining rationale, creating opacity and lack of transparency for end users.
problem_evidence: resulting in lack of transparency between human and machine | Concerns are growing over the opacity of such complex AI models, particularly deep learning architectures
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.
limitation: XAI field lacks a universal definition, faces a trade-off between accuracy and interpretability, and has no standardized evaluation metrics.
tag: Evidence-backed problem
key_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.
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:
- peer_reviewed | Algorithms | https://doi.org/10.3390/a18090556 | 2025-09-03
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

2117b04fab1322279853b487a8eafce27df5b107cd8c2f566f6d53430c024fea

previous

0000000000000000000000000000000000000000000000000000000000000000