Connecting <scp>AI</scp> , Explainability and Semantic in Animal Applications: A Scoping Review

Abstract

ABSTRACT Artificial intelligence (AI) is increasingly adopted in animal‐related domains such as health monitoring, behaviour analysis and welfare assessment. However, concerns about the transparency and interoperability of AI outputs are rising. This scoping review investigates how AI and explainable AI (XAI) are applied in animal‐related systems and examines the role of semantic technologies in enhancing their interoperability. In this review, we followed PRISMA‐ScR guidelines and conducted five structured searches across ScienceDirect, Springer, Scopus, IEEE Xplore and Web of Science. The searches targeted AI applications (S 1 ), XAI applications (S 2 ) and the use of semantic knowledge in AI (S 3 ) and XAI (S 4 ). Studies were screened, assessed using the QualSyst tool and selected based on Q1/Q2 SJR or CORE C–A* classification. A total of 21 review papers were selected for AI applications and 8 for XAI. No eligible papers were found regarding the use of semantics in AI or XAI. While explainability is gaining attention, it remains mostly limited to visual or statistical tools, lacking domain‐specific contextualization. We observed that AI is now pervasive in animal‐related research, yet XAI practices remain underdeveloped and lack semantic grounding. This gap calls for ontology‐based explainability to enhance trust, relevance and usability for both experts and non‐specialists.

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