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.
Professional Judgment in an Era of Artificial Intelligence and Machine Learning
Frank Pasquale is an expert on the law of artificial intelligence, algorithms, and machine learning. He has been recognized as one of the ten most cited scholars in health law in the United States. His book The Black Box Society: The Secret Algorithms that Control Money and Information (2015) develops a social theory of reputation, search, and finance, and offers pragmatic reforms to improve the information economy. In privacy law and surveillance, his work focuses on the regulation of algorithmic ranking, scoring, and sorting systems, including credit and threat scoring.
Frank Pasquale; Professional Judgment in an Era of Artificial Intelligence and Machine Learning. boundary 2 1 February 2019; 46 (1): 73–101. doi: https://doi.org/10.1215/01903659-7271351
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