Explainable AI for Industry 5.0: Shedding light on the black box
Abstract
The rapid development of artificial intelligence (AI) is accompanied by increasing computational complexity and decreasing model transparency, which significantly limits its adoption in critical domains that require a high level of trust, interpretability, and justification of decisions. Under these conditions, the field of Explainable Artificial Intelligence (XAI) has gained particular importance as it focuses on approaches and technologies that enable understanding of AI system logic and interpretation of their outputs. This article examines the timely topic of implementing XAI in the context of Industry 5.0. Special attention is given to practical application scenarios: the authors present concrete industrial cases from IBM, Siemens, and other companies demonstrating how XAI contributes to enhancing the reliability, safety, efficiency, and trustworthiness of AI systems. The study includes a systematic search and analysis of the literature in this domain and proposes well-grounded key criteria for comparing existing XAI approaches. The article also outlines the advantages, current limitations, and promising directions for the development of XAI, highlighting the opportunities it opens for improving effectiveness, transparency, and trust in business.
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References
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