Symbols and Neurons: A Review of Symbolic XAI in Deep Learning

Abstract

Background: Deep neural networks increasingly power language, vision, and decision systems, yet many deployments require explanations that are faithful, compositional, and governance-ready. Symbolic techniques promise these properties, but the literature mixes post-hoc extraction, knowledge injection, and intrinsically hybrid designs without a unifying view.

Objectives: We provide a systematic review and synthesis of symbolic explainable AI (XAI) for deep learning (January 2017– June 2025), organize the field around a three-part taxonomy—Symbolic Knowledge Extraction (SKE), Symbolic Knowledge Injection (SKI), and Hybrid neurosymbolic architectures—and propose a conceptual framework that clarifies training–inference flows, explanation interfaces, human feedback, and governance touchpoints.

Methods: Beginning from ≈50,000 records, we deduplicated and screened full texts, analyzed 393 PDFs, and included 273 primary studies in the synthesis. We coded each paper for model domain, modality, symbolic formalism, explanation scope and stage, evaluation protocol, and governance alignment. Analyses combine descriptive statistics with stratification by domain and formalism; we qualitatively assess evidence for faithfulness, robustness, data efficiency, and constraint satisfaction.

Results: Research activity accelerates after 2020, with a marked turn toward hybrids. Across the corpus, SKE, SKI, and Hybrid account for approximately 29%, 26%, and 45% of studies, respectively. Rule sets/decision trees remain the dominant explanation artifacts, while logic- and program-based formalisms grow in NLP and planning. SKI most often targets constraint satisfaction and robustness improvements; SKE emphasizes global surrogates and faithfulness auditing; hybrids report gains in sample efficiency and traceable reasoning. However, evaluation practices are heterogeneous, human-subject studies are scarce, and explicit links to policy/risk controls appear in a minority of works.

Conclusions: Our framework unifies how data, priors, and symbolic reasoning interact with neural learners, the explanation interface, human stakeholders, and governance. We distill actionable recommendations: (1) report faithfulness and constraintsatisfaction metrics alongside accuracy; (2) specify symbolic assumptions and training-time injections precisely; (3) include user studies or auditor-centric protocols for high-stakes use; and (4) develop benchmarks that couple tasks with machinereadable knowledge bases. We highlight open problems in scalable formal reasoning with foundation models, verifying generated rationales, and measuring causal faithfulness at scale.