Professional Judgment in an Era of Artificial Intelligence and Machine Learning

Though artificial intelligence (AI) in healthcare and education now accomplishes diverse tasks, there are two features that tend to unite the information processing behind efforts to substitute it for professionals in these fields: reductionism and functionalism. True believers in substitutive automation tend to model work in human services by reducing the professional role to a set of behaviors initiated by some stimulus, which are intended to accomplish some predetermined goal, or maximize some measure of well-being. However, true professional judgment hinges on a way of knowing the world that is at odds with the epistemology of substitutive automation. Instead of reductionism, an encompassing holism is a hallmark of professional practice—an ability to integrate facts and values, the demands of the particular case and prerogatives of society, and the delicate balance between mission and margin. Any presently plausible vision of substituting AI for education and health-care professionals would necessitate a corrosive reductionism. The only way these sectors can progress is to maintain, at their core, autonomous professionals capable of carefully intermediating between technology and the patients it would help treat, or the students it would help learn

Indicators for managing human centred manufacturing

Establishing indicators for managing human factors (HF) aspects in the design of production systems remains a challenge. We address the problem in two dimensions – firstly, what aspects of HF are to be considered, and secondly, where in the development process HF is to be measured. In these dimensions a large number of HF metrics are possible in the perceptual, cognitive, physical and psychosocial domains of HF. The relevance of these measures to injury, productivity, quality and organizational strategy continue to be poorly understood. From this perspective we make propositions on the need for: 1) strategic HF metrics selection, 2) metrics application throughout the development process, 3) predictive ‘virtual’ HF metrics approaches, 4) metrics based design guidelines, 5) connecting metrics with design choices and strategies, 6) integrating HF metrics within existing approaches, 7) continuous improvement of the metrics system, and 8) the need to evaluate metrics system quality