Non-symbolic explanation methods have dominated XAI for a decade but lack rigor required for high-stakes ML decisions (Huang, arXiv:2604.15898). The paper identifies provable shortcomings in Shapley value applications such as SHAP that can mislead human decision makers and overviews symbolic alternatives for provably correct relative feature importance (Huang, arXiv:2604.15898; Lundberg and Lee, arXiv:1705.07874).

Original coverage of SHAP-based tools has missed their axiomatic inconsistencies when applied to certain model classes and has under-emphasized alignment with regulatory demands such as the EU AI Act's transparency requirements for high-risk systems. Related formal XAI literature demonstrates that symbolic techniques using satisfiability solvers deliver exact explanations with computational trade-offs, connections the primary source only partially develops (Ignatiev et al., arXiv:1811.10652).

Patterns from prior XAI shifts show that usability-focused methods repeatedly encounter reproducibility failures under distribution shift; synthesis of the cited sources indicates symbolic rigor directly addresses the accountability gap for enterprises and regulators as opaque models scale, supplying provable guarantees absent in prevailing non-symbolic practice.