# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 5 Nomological Deductive Reasoning (NDR): A Proposed Solution
### Ontology Design
#### 5.3.3 Experimental Results
The full code of this comparison is available at [[https://github.com/[PERSON]/NDR-Framework-Explainable-AI](https://github.com/[PERSON]/NDR-Framework-Explainable-AI)]([https://github.com/[PERSON]/NDR-Framework-Explainable-AI](https://github.com/[PERSON]/NDR-Framework-Explainable-AI)). On the other hand, as illustrated in Figure 10b, the prediction of \"Bad Credit\" for Instance #4 is accompanied by explanations generated from six different interpretability frameworks, each offering varying degrees of clarity and utility. Among these, the **Nomological Deductive Reasoning (NDR)** approach delivers the most structured and contextually grounded explanation. It explicitly attributes the adverse credit decision to the absence of a checking account, referencing domain-specific banking norms that associate such financial behavior with increased risk. This reasoning aligns closely with human cognitive expectations, offering both traceability and normative justification. In contrast, the **Causal Inference explanation** identifies statistically relevant factors--such as younger age (<25) being linked to a higher default risk and stable employment mitigating risk--but lacks decisiveness in synthesizing these competing signals into a coherent conclusion. Similarly, the **Neuro-Symbolic explanation** emphasizes employment stability as a favorable \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Value** & **Description** \\ \hline Accuracy & 97\% & Correct classification of creditworthiness. \\ \hline Rule Coverage & 1.00 & Fraction of predictions supported by domain rules in the KB. \\ \hline Rule-consistent & 300/300 & Number of predictions fully explainable by symbolic logic. \\ \hline Mismatch Penalty & 0 & Instances where statistical outputs conflicted with KB rules. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the NDR framework experiment results. indicator but does not resolve the tension between this positive factor and the final negative prediction. The **Knowledge Graph explanation** constructs a semantic path that includes both risk (absence of a checking account) and security (property ownership) yet stops short of articulating how these are weighed in the final decision. The **LLM-style explanation**, while human-readable, primarily restates input features without embedding them in a domain-specific interpretive framework. Lastly, the **LIME explanation** provides local feature-importance scores, showing that a \"ForeignWorker\" status exerted the strongest negative influence, but it does not offer a global rationale or domain-based interpretation. Overall, the NDR explanation distinguishes itself by integrating formal logic with domain-relevant rules, enabling it to generate actionable, transparent justifications. This makes NDR particularly suitable for high-stakes applications, where explanation fidelity and alignment with institutional norms are critical. From the illustrations in Figure 10a,b, the trustworthiness of NDR-generated explanations stems, first, from their foundation in formal deductive reasoning. Unlike post hoc or correlation-based techniques, NDR explanations are derived from domain-specific causal laws and antecedent conditions encoded in a structured knowledge base. This ensures that each prediction is not only statistically plausible but also epistemically justified. The deductive inference engine enforces consistency by penalizing predictions that violate predefined rules, thereby improving reliability. The empirical results demonstrated that a high proportion of the model outputs remained consistent with the knowledge base, validating the explanatory rigor of the framework. Second, human-readability is achieved through structured natural-language explanations that mirror human causal reasoning. Explanations are delivered in an \"if-then\" format, with clearly articulated reasons based on interpretable features.