aceA 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…
Black-box sub-symbolic AI, particularly deep learning, computes predictions without explaining rationale, creating opacity and lack of transparency for end users.
XAI field lacks a universal definition, faces a trade-off between accuracy and interpretability, and has no standardized evaluation metrics.
Evidence
- Peer-reviewedAlgorithms2025-09-03
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Truvace Impact Record TRV-2026-0489, v1: “A Review of Explainable Artificial Intelligence from the Perspectives of Challenges and Opportunities.” Truvace, 2026-07-22. /record/TRV-2026-0489 (accessed at citation time). sha256 2117b04fab132227…
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