Neuro-Symbolic AI for Fair and Verifiable Decision-Making in High-Stakes Domains: A Literature Review

Artificial intelligence is now deployed across high-stakes domains such as healthcare diagnosis, criminal justice risk assessment, and financial credit scoring, where fairness, transparency, and accountability matter a great deal but are frequently compromised in practice. This paper reviews recent literature at the intersection of neuro-symbolic AI, algorithmic fairness, and formal verification of machine learning systems. It draws on twenty-five peer-reviewed articles, conference and workshop papers, and preprints, most published between 2022 and 2026, organized around five themes: documented bias and fairness concerns in AI applications for healthcare, criminal justice, and finance; the complementary strengths and limitations of purely neural and purely symbolic reasoning; the emergence of neuro-symbolic AI as a hybrid paradigm; the shift from post-hoc bias correction toward fairness built in by design; and formal verification techniques for certifying safety and fairness properties of neural and hybrid systems. Across these themes, a recurring gap emerges, one that several of the systematic reviews included in this synthesis document with their own numbers: verification, proactive fairness, and explainability are rarely built into a single neuro-symbolic architecture, and are usually treated as separate concerns addressed only after a system is deployed. The paper discusses what an architecture closing that gap might look like and points to directions for future work. This is a narrative, curated review rather than a systematic review carried out under one PRISMA-style multi-database search protocol; that scope is stated explicitly in the methodology and limitations sections.