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Advisor(s)
Abstract(s)
As intelligent systems become increasingly embedded in industrial ecosystems, the demand for transparency, reliability, and interpretability has intensified. This study investigates
how explainable artificial intelligence (XAI) contributes to enhancing accountability, trust, and human–machine collaboration across industrial contexts transitioning from Industry
4.0 to Industry 5.0. To achieve this objective, a systematic bibliometric literature review (LRSB) was conducted following the PRISMA framework, analysing 98 peer-reviewed
publications indexed in Scopus. This methodological approach enabled the identification of major research trends, theoretical foundations, and technical strategies that shape the
development and implementation of XAI within industrial settings. The findings reveal that explainability is evolving from a purely technical requirement to a multidimensional
construct integrating ethical, social, and regulatory dimensions. Techniques such as counterfactual reasoning, causal modelling, and hybrid neuro-symbolic frameworks are shown
to improve interpretability and trust while aligning AI systems with human-centric and legal principles, notably those outlined in the EU AI Act. The bibliometric analysis further
highlights the increasing maturity of XAI research, with strong scholarly convergence around transparency, fairness, and collaborative intelligence. By reframing artificial intelligence through the lens of transparent machine reasoning, this study contributes to both theory and practice. It advances a conceptual model linking explainability with measurable indicators of trustworthiness and accountability, and it offers a roadmap for developing responsible, human-aligned AI systems in the era of Industry 5.0. Ultimately, the study underscores that fostering explainability not only enhances functional integrity but also strengthens the ethical and societal legitimacy of AI in industrial transformation.
Description
Keywords
Artificial intelligence Explainable AI Industry Industry 4.0 Industry 5.0 Decision making
Pedagogical Context
Citation
Rosário, A. T., & Dias, J. C. (2025). Illuminating industry evolution: reframing artificial intelligence through transparent machine reasoning. Information, 16(12), 1044. https://doi.org/10.3390/info16121044
