There are two fundamental features of the information processing behind most efforts to substitute artificial intelligence, machine learning, and robotics for professionals in health and education: reductionism and functionalism. However, true professional judgment hinges on a way of knowing the world and relating to persons that is at odds with the mindset of substitutive automation. Instead of reductionism, an encompassing holism is a hallmark of professional practice — an ability to integrate facts and values, to respect the demands of the particular case and prerogatives of society, and to balance mission and margin in institutional decision-making. Any presently plausible vision of substituting artificial intelligence for education and health care professionals would be premised on patients and students accepting services as “medical care” or “education” that are often far inferior to what a skilled, reflective practitioner in either field could provide. The only way these sectors can progress is to maintain, at their core, a large (and likely growing) core of professionals capable of carefully intermediating between technology and the patients it would help treat, or the students it would help learn. As critical data studies have repeatedly shown, the lifeblood of AI ambitions — data — is neither brute nor given. Deciding what data matters, how it is fairly and accurately collected, and how to balance quantitative and qualitative approaches to the representation of situations, will be critical and enduring roles for professionals.

中文翻译：

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人工智能和机器学习时代的专业判断

大多数为健康和教育领域的专业人员替代人工智能、机器学习和机器人技术的努力背后的信息处理有两个基本特征：还原论和功能主义。然而，真正的专业判断取决于了解世界和与人交往的方式，这与替代自动化的心态不一致。与还原论不同，包容性整体论是专业实践的标志——整合事实和价值观的能力，尊重特定案例的要求和社会特权，以及在机构决策中平衡使命和利润的能力。任何目前用人工智能取代教育和医疗保健专业人员的合理愿景都以患者和学生接受作为“医疗保健”或“教育”的服务为前提，而这些服务通常远不如任何一个领域的熟练、深思熟虑的从业者所能提供的服务。这些部门取得进步的唯一途径是在其核心保持大量（并且可能正在增长）的专业核心人员，他们能够在技术与它所帮助治疗的患者或它所帮助学习的学生之间进行谨慎的中介。正如关键数据研究反复表明的那样，人工智能野心的命脉——数据——既不是粗暴的，也不是给定的。决定哪些数据很重要，如何公平准确地收集数据，以及如何平衡定量和定性方法来表示情况，