Automation, Epistemology of Automation, Taylorism, Disruptive AI, Algorithmic Decision Making, Clinical Practice Guidelines, Computational Thinking, Rankings, Metrics, Data-Driven
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.
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Law | Law and Philosophy | Legal Ethics and Professional Responsibility | Legal Profession
Digital Commons Citation
Pasquale, Frank A., "Professional Judgment in an Era of Artificial Intelligence and Machine Learning" (2019). Faculty Scholarship. 1614.