Mental workload critically affects well-being and performance in safety-critical systems. While machine learning models for mental workload prediction often leverage physiological indicators, interpretability and error analysis are frequently overlooked. This study develops robust models for workload prediction that emphasize interpretability and analyzes common misclassifications to elucidate key mechanisms. Respiratory and cardiac signals from 30 participants, as well as oculomotor signals from 17 participants, captured under varying task demands were utilized. Five models of varying interpretability were validated with optimized hyperparameters and preprocessing. A logistic regression and a decision tree were selected to distinguish between two and three workload levels, respectively. On unseen test data, they achieved f1-scores of 90.5% (accuracy: 92.2%) and 72.0% (accuracy: 72.3%). Performance varied across scenarios and individuals. Findings show that transparent, efficient models combined with appropriate preprocessing can compete with black-box approaches, with implications for safety-critical applications where interpretability, trust, and computational efficiency are essential.

Human control over artificial agents is of increased importance today given the recent advances of artificial intelligence and its impact on today’s societies. This makes interdisciplinary cooperation especially between human-factors/ergonomics and artificial intelligence an important challenge that needs to be addressed. In addition to the common bridge that exists between human-factors/ergonomics and artificial intelligence, i.e., the balanced integration of humans and artificial agents into one loop, we reinterpret the work of cognitive systems engineering and resilience engineering as human-machine intelligence. The goal of human-machine intelligence is to understand and create intelligence in the homo machina, an intelligent organism made of human- and artificial agents. We define intelligence in a way that highlights decision making and adaptation and apply this definition to the design of the homo machina. This creates a unified viewpoint which fosters a shared approach between human-factors/ergonomics, artificial intelligence, and cognitive psychology.