Enhancing the trustworthiness of explainable artificial intelligence through fuzzy logic and ontology

The insufficient explainability of machine learning models has long constituted a significant challenge in the field. Specialists across various domains of artificial intelligence (AI) application have endeavored to develop explicable and reliable systems. To address this challenge, DARPA formulated a contemporary approach to explainable AI (XAI). Subsequently, Bellucci et al. expanded DARPA's XAI concept by proposing a novel methodology predicated on semantic web technologies. Specifically, they employed OWL2 ontologies for the representation of user-oriented expert knowledge. This system enhances confidence in AI decisions through the provision of more profound explanations. Nevertheless, XAI systems continue to encounter difficulties when confronted with incomplete and imprecise data. We propose a novel approach that utilizes fuzzy logic to address this limitation. Our methodology is founded on the integration of fuzzy logic and machine learning models to imitate human thinking. This new approach more effectively interfaces with expert knowledge to facilitate deeper explanations of AI decisions. The system leverages expert knowledge represented through ontologies, maintaining full compatibility with the architecture proposed by Bellucci et al. in their work. The objective of this research is not to enhance classification accuracy, but rather to improve the trustworthiness and depth of explanations generated by XAI through the application of "explanatory" properties and fuzzy logic.

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