# 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
-Installmentrate:Amanageableinstallmentrate,whichindicatesfinancialstability. -Duration:Theloandurationismorethan12 months,whichindicatesapositivesignforcredit. Prediction:BadCredit Explanation: Themodelpredictsa**BadCredit** because,basedon**bankinglaws**, -Otherdebtors:Thepersonhasmultiplefinancial obligations,whichincreasesfinancialrisk. -Purpose:Thecreditpurposeisconsideredrisky,contributingtoanegativecreditprediction. CorrectPredictionsbasedonKBlogic:300 RuleCoverage:1.00 MismatchPenalty:0
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Alignment with Domain Knowledge

The deductive consistency of the model's outputs was validated by comparing its generated explanations with the logic encoded in the Knowledge Base. The following were found:

* **Rule Consistency:** All 300 predictions were logically consistent with the rules defined in the Knowledge Base. * **Rule Coverage:** Every prediction was fully accounted for by one or more symbolic rules, resulting in a **Rule Coverage = 1.00**, meaning 100% of rule coverage before decision-making. * **Mismatch Penalty:** There were no mismatches between the model's explanations and the rule base, indicating a Mismatch Penalty of 0, reflecting 100% loyalty to the rule base. Summary of Results

Table 3 summarizes the key evaluation metrics, highlighting the NDR framework's ability to combine high predictive performance with complete logical consistency and rule-based explainability. Figure 9: Sample explanations generated by the NDR framework for credit-risk predictions based on the German Credit dataset [33]. Each explanation illustrates how specific input features are mapped to domain rules defined in the Knowledge Base, enabling transparent, causal reasoning behind the model’s decision. The examples demonstrate both positive and negative classifications, with logical justifications grounded in financial regulations. These results demonstrate that NDR not only preserves predictive accuracy but also guarantees that every decision is logically grounded, transparent, and normatively aligned--a crucial requirement for AI adoption in high-stakes domains such as finance. #### 5.3.4 Evaluation of the Explanatory Quality of the NDR Framework

To assess the quality of explanatory outputs generated by the NDR framework, we focused on three key dimensions of explainability: trustworthiness, human-readability, and actionability. These criteria were selected based on a comprehensive review of the literature and the practical needs of high-stakes domains like finance, where decisions must be interpretable, transparent, and actionable. Consider the following explanation produced by NDR for a credit application decision: _\"The model predicts a \"Bad Credit\" because based on banking laws, the applicant has multiple financial obligations which increases the financial risk, and the credit purpose is considered risky, contributing to a negative credit prediction\"_. This explanation scores highly on trustworthiness, as it aligns with established lending criteria and domain-specific reasoning. It also exhibits strong human-readability, using plain language and avoiding technical jargon, which makes it accessible to non-technical users. Finally, the explanation is clearly actionable, offering concrete steps the applicant can take to influence future outcomes (reducing financial obligations such as clearing his existing debts and properly motivating the purpose of the loan). This kind of output reflects the core strengths of the NDR framework and illustrates how it outperforms alternative methods that may produce less coherent, overly technical, or non-instructive explanations vis-a-vis the end user, who may be a simple lay consumer. A comparative qualitative analysis of NDR alongside other emerging methods--LIME, causal inference models, neuro-symbolic reasoning, knowledge graph-based approaches, and LLM-driven narratives--is presented in Figure 10a,b, using consistent instances to highlight interpretive differences.