Narrative Review on Symbolic Approaches for Explainable Artificial Intelligence: Foundations, Challenges, and Perspectives

Abstract

The review “Symbolic Approaches for Explainable Artificial Intelligence” discusses the potential of symbolic AI to improve transparency, contrasting it with opaque deep learning systems. Though connectionist models perform well, their poor interpretability means that they are of concern for bias and trust in high-stakes fields such as healthcare and finance. The authors integrate symbolic AI methods—rule-based reasoning, ontologies, and expert systems—with neuro-symbolic integrations (e.g., DeepProbLog). This paper covers topics such as scalability and integrating knowledge, proposing solutions like dynamic ontologies. The survey concludes by advocating for hybrid AI approaches and interdisciplinary collaboration to reconcile technical innovation with ethical and regulatory demands.

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