Biases in AI-Supported Industry 4.0 Research: A Systematic Review, Taxonomy, and Mitigation Strategies

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Detalles Bibliográficos
Publicado en:	Applied Sciences vol. 15, no. 20 (2025), p. 10913-10934
Autor principal:	Arévalo-Royo, Javier
Otros Autores:	Francisco-Javier, Flor-Montalvo, Juan-Ignacio, Latorre-Biel, Jiménez-Macías Emilio, Martínez-Cámara, Eduardo, Blanco-Fernández, Julio
Publicado:	MDPI AG
Materias:	Industrial engineering Big Data Simulation Research methodology Design of experiments Artificial intelligence Hypotheses Scientific method Decision making Industrial Internet of Things Taxonomy Data analysis Natural language processing Literature reviews Algorithms Research & development > R&D Industry 4.0 Engineering research Systematic review Bias
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	Industrial engineering research has been reshaped by the integration of artificial intelligence (AI) within the framework of Industry 4.0, characterized by the interplay between cyber-physical systems (CPS), advanced automation, and the Industrial Internet of Things (IIoT). While this integration opens new opportunities, it also introduces biases that undermine the reliability and robustness of scientific and industrial outcomes. This article presents a systematic literature review (SLR), supported by natural language processing techniques, aimed at identifying and classifying biases in AI-driven research within industrial contexts. Based on this meta-research approach, a taxonomy is proposed that maps biases across the stages of the scientific method as well as the operational layers of intelligent production systems. Statistical analysis confirms that biases are unevenly distributed, with a higher incidence in hypothesis formulation and results dissemination. The study also identifies emergent AI-related biases specific to industrial applications such as predictive maintenance, quality control, and digital twin management. Practical implications include stronger reliability in predictive analytics for manufacturers, improved accuracy in monitoring and rescue operations through transparent AI pipelines, and enhanced reproducibility for researchers across stages. Mitigation strategies are then discussed to safeguard research integrity and support trustworthy, bias-aware decision-making in Industry 4.0.
ISSN:	2076-3417
DOI:	10.3390/app152010913
Fuente:	Publicly Available Content Database