Machine learning algorithms present substantial promise for more effective decision-making by administrative agencies. However, some of these algorithms are inscrutable, namely, they produce predictions that humans cannot understand or explain. This trait is in tension with the emphasis on reason-giving in administrative law. The article explores this tension, advancing two interrelated arguments. First, providing adequate reasons is a significant facet of respecting individuals’ agency. Incorporating inscrutable algorithmic predictions into administrative decision-making compromises this normative ideal. Second, as a long-term concern, the use of inscrutable algorithms by administrative agencies may generate systemic effects by gradually reducing the realm of the humanly explainable in public life, a phenomenon Max Weber termed ‘re-enchantment’. As a result, the use of inscrutable machine learning algorithms might trigger a special kind of re-enchantment, making us comprehend less rather than more of shared human experience, and consequently altering the way we understand the administrative state and experience public life.

中文翻译：

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机器学习与行政国家的复兴

机器学习算法为行政机构做出更有效的决策提供了巨大的希望。然而，其中一些算法是难以理解的，即它们产生人类无法理解或解释的预测。这一特征与行政法中强调给出理由是有冲突的。本文探讨了这种紧张关系，提出了两个相互关联的论点。首先，提供充分的理由是尊重个人能动性的一个重要方面。将难以理解的算法预测纳入行政决策会损害这一规范理想。其次，作为一个长期问题，行政机构使用难以理解的算法可能会逐渐缩小公共生活中人类可解释的范围，从而产生系统性影响，马克斯·韦伯将这种现象称为“重新迷惑”。