Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
Open accessAbstract
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable reasoning that is aligned with domain-specific logic. This paper presents Nomological Deductive Reasoning (NDR), supported by Nomological Deductive Knowledge Representation (NDKR), as a framework aimed at improving the transparency and auditability of AI decisions through the integration of formal logic and structured domain knowledge. NDR enables the generation of causal, rule-based explanations by validating statistical predictions against symbolic domain constraints. The framework is evaluated on a credit-risk classification task using the Statlog (German Credit Data) dataset, demonstrating that NDR can produce coherent and interpretable explanations consistent with expert-defined logic. While primarily focused on technical integration and deductive validation, the approach lays a foundation for more transparent and norm-compliant AI systems. This work contributes to the growing formalization of XAI by aligning statistical inference with symbolic reasoning, offering a pathway toward more interpretable and verifiable AI decision-making processes.
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The impact of Generative AI cannot be understood merely by measuring performance differences but must also be framed as part of broader algorithmic design questions about how AI affords or limits different modes of human reasoning.
“These perspectives underscore that the impact of Generative AI cannot be understood merely by measuring performance differences but must also be framed as part of broader algorithmic design questions about how AI affords or limits different modes of human reasoning.”
From Exploring the Impact of AI Tools on Cognitive Skills: A Comparative Analysis (2025) · cites this paper as [48]
The Introduction and §4 argue that AI should be assessed for transparency and human interpretability, not performance alone; §2.5 evaluates both accuracy and interpretability. But the paper addresses explainable AI and the accessibility of AI explanations, not the impact of generative AI on different modes of human reasoning. The copy provides no page numbers for these passages. (LLM, conf=0.85)
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ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification
Almutairi, M., Stahl, F. T., & Bramer, M. (2021) · IEEE Access · doi:10.1109/access.2021.3062763 · Cited by 14 · see citing works
Generation of Textual Explanations in XAI: the Case of Semantic Annotation
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From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Nguyen, V. B., Schlötterer, J., & Seifert, C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_4 · Cited by 14 · see citing works
Towards a Model Independent Method for Explaining Classification for Individual Instances
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LLMs for XAI: Future Directions for Explaining ExplanationsPreprint
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Neurosymbolic Knowledge Representation for Explainable and Trustworthy AIPreprint
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Natural Language Explanations for Machine Learning Classification Decisions
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A Psychological Theory of ExplainabilityPreprint
Yang, S. C., Folke, T., & Shafto, P. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2205.08452 · Cited by 6 · see citing works
Post-hoc Explanation using a Mimic Rule for Numerical Data
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Exploring the Effect of Explanation Content and Format on User Comprehension and Trust in HealthcarePreprint
Rago, A., Pálfi, B., Sukpanichnant, P., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.17401
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Exploring the Impact of AI Tools on Cognitive Skills: A Comparative Analysis
Musazade, N., Mezei, J., & Wang, X. (2025) · Algorithms · doi:10.3390/a18100631 · Cited by 4 · see citing works
“Furthermore, frameworks such as nomological deductive reasoning stress the importance of combining transparent logic with probabilistic inference to ensure that AI assistance supports rather than constrains human cognitive processes [48].”
Latif, Z., Mahmood, S., & Sikder, M. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0216-2.ch007
- Authors
- Hakizimana, G., & Ledezma Espino, A.
- Affiliations
- Universidad Carlos III de Madrid (ES)
- Department of Computer Science & Engineering, Universidad Carlos III de Madrid, 28911 Leganes, Spain
- Venue
- Algorithms
- Publisher
- MDPI AG
- Published
- 2025-05-23
- Volume
- 18
- Issue
- 6
- Pages
- 306
- ISSN
- 1999-4893 (ISSN-L)
- OpenAlex
- W4410643594
- Cited by
- 2
- References
- 98
- Citations by year
- 2026: 1 · 2025: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Algorithms (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- MCIN/AEI 10.13039/501100011033 and the European Union (PID2021-124335OB-C22)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, formal logic, knowledge representation, causal explanation, rule-based explanations, auditability, AI transparency, credit risk classification, large language models, symbolic reasoning, trustworthy AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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