# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 4 Twofold Need for Improvement in XAI Research
### The Need for Effective and Efficient Integration of Explanation Theory into Explainability Design  
As explicit knowledge representation emerges as a candidate solution to the first facet of explainable AI--enabling AI systems that are inherently capable of being explained, as discussed and illustrated in Figure 6--the theory of explanation presents itself as the complementary solution to the second critical facet: generating human-compatible explanations grounded in explanation constructs.  
A critical challenge in current XAI methods is the insufficient integration of formal explanation theories, which contributes to opaque decision-making in AI systems. Without a clear theoretical foundation, these systems often lack transparency, limiting the traceability and auditability of their reasoning processes. While some efforts have incorporated explanation theories--yielding promising results--the absence of a widely accepted, foundational framework within the XAI community remains a major obstacle.  
Several scholars have advocated integrating causal explanation theories, particularly [PERSON]'s framework of causal inference [127], which models relationships using Bayesian networks [128] and supports counterfactual reasoning through directed acyclic graphs (DAGs). These tools enable mathematically rigorous intervention-based explanations. However, their complexity often hinders accessibility for non-experts, limiting their ability to foster trust. The transparency offered by causal models is indirect, relying on a user's capacity to interpret statistical relationships and manipulate causal structures.  
Other approaches, such as Granger causality [129], causal discovery algorithms [130], and structural causal models [131], have also been explored for their potential to enhance interpretability. Granger causality, while useful for detecting temporal dependencies in time-series data, offers limited support for non-temporal causal reasoning and often lacks intuitive interpretability. Similarly, causal discovery methods--though promising in extracting causal graphs from data--depend heavily on assumptions (e.g., no hidden confounders), and the resulting models are typically opaque to end-users without domain expertise.  
Collectively, these methods underscore the promise of causal reasoning for improving explainability and trust in AI. Yet their effectiveness is contingent upon translating complex mathematical outputs into accessible, human-understandable explanations--a challenge that remains unresolved in current XAI practices.  
In sum, advancing explainability in AI requires a deliberate and systematic integration of explanation theory. In scientific disciplines, theory serves as the backbone of research, guiding experimental design, methodological development, and result interpretation [132; 133]. Without it, explainability research risks becoming fragmented and methodologically incoherent. The lack of a unified theoretical framework has led to ad hoc and domain-specific XAI solutions that are often incapable of delivering transparent reasoning or actionable insights.  
This is particularly problematic in high-stakes domains such as healthcare, finance, transportation, and law, where understanding why an AI system makes a decision is as crucial as understanding what that decision is. To achieve truly interpretable and end-to-end explainable AI, the field must adopt a general, formal theory of explanation as the foundation for method development and evaluation. Only then can XAI systems consistently produce explanations that are transparent, trustworthy, and aligned with domain knowledge.