# 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
For example:

**Prediction**: \"Good Credit\"

**Explanation:**

* \"The model predicts a 'Good Credit' because, based on banking laws, the duration of the loan is longer than 12 months, which indicates a positive sign for credit, and the installment rate is manageable, which suggests financial stability\". which is the more human-friendly form adapted by NDR explanation layer from:

* \"If the duration of the loan is longer than 12 months, which indicates a positive sign for credit, and if the installment rate is manageable, suggesting financial stability, then the model predicts a 'Good Credit' based on banking laws\". These explanations are designed to highlight key variables, offer concise reasoning, and use plain language free of technical jargon. Although a formal user study is planned for future work, the current framework was developed with interpretability and end-user accessibility as guiding principles. Third, the actionability of NDR outputs is supported by their alignment with modifiable input features and real-world financial decision logic. By identifying contributing factors (e.g., _installment rate, loan duration, personal status_) and explaining their relevance through legal or policy-based rules, the explanations offer users a pathway for improving or justifying outcomes. This makes them particularly useful in domains where decision support is essential. To summarize the quality of explanations, Table 4 outlines the relationship between NDR's design mechanisms and the corresponding dimensions of explainability:
Figure 10: (a) Comparative explanation outputs for an instance number # predicted as **Good Credit* using six interpretability methods: Nomological Deductive Reasoning (NDR), Causal Inference, Neuro

Symbolic Reasoning, Knowledge Graph Paths, LLM-style explanations, and LIME. NDR stands out by providing a structured, law-based rationale for predictions, offering enhanced transparency compared with the data-driven feature attributions and descriptive summaries from other approaches. **(b)** Comparative explanation outputs for an instance number # predicted as **Bad Credit** using six interpretability methods: Nomological Deductive Reasoning (NDR), Causal Inference, Neuro-Symbolic Reasoning, Knowledge Graph Paths, LLM-style explanations, and LIME. While the LIME and LLM approaches highlight influential features and descriptive summaries, only NDR provides a normative, rule-based justification anchored in domain knowledge, thereby enhancing interpretability and decision transparency. These findings reinforce that NDR not only enhances model transparency but also improves the practical utility of AI systems in real-world contexts. By combining formal logic with domain-grounded reasoning, the framework closes the transparency gap identified in current XAI methods. ## 6 Discussion and Future Work

The initial experimental findings demonstrate the efficacy of the Nomological Deductive Reasoning (NDR) framework in integrating structured domain knowledge into machine learning models for credit scoring. By formalizing and embedding financial principles as logical rules, the NDR framework provides transparent, interpretable, and human-aligned explanations for model predictions. This approach not only bridges the gap between data-driven modeling and domain expertise but also offers pathways to enhance the trustworthiness of AI systems by offering explanations that are logically consistent and verifiable. A key strength of the NDR framework lies in its ability to generate explanations that align with human reasoning, particularly in high-stakes domains such as finance, where regulatory compliance, user trust, and interpretability are paramount. From the financial experiment, the explanations derived from the NDR model are not only readable and actionable but also grounded in established financial logic, offering stakeholders clarity on why certain predictions are made.