How to account artificial intelligence in human factor analysis of complex systems?

Human factors analysis (HFA) has been explored from various aspects (e.g., engineering, psychology, physiology, and ergonomics). Numerous conventional techniques have been developed and applied to improve system safety from the human perspective. However, emerging sociotechnical systems, industry 4.0, and the use of artificial intelligence-driven systems reveal these methods' incapability. This necessity is developing intelligent approaches that account for integrating artificial intelligence (AI) into human factors. This work reviewed the integration of artificial intelligence and expert systems into HFA. It primarily focused on using machine and deep learning and knowledge/data-driven modeling approaches to HFA. Accordingly, this systematic review investigated the applications, contributions, challenges, and research gaps of HFA in complex systems. We analyzed seven vital elements of HFA to illustrate these concerns. This work also highlighted important myths, misapplications, and critical concerns that need to be addressed using advanced approaches.

Automated summary

Human factors analysis has been explored from various aspects, but conventional techniques are incapable of addressing the challenges posed by emerging sociotechnical systems and AI-driven systems. This work reviewed the integration of artificial intelligence and expert systems into HFA, specifically focusing on machine learning, deep learning, and knowledge/data-driven modeling approaches. The study investigated the applications, contributions, challenges, and research gaps of HFA in complex systems, highlighting important concerns and the need for advanced approaches.