[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118844-en":3,"doc-seo-118844-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118844,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning and the Re-Enchantment of the Administrative State - Article","Machine learning algorithms can improve administrative decision-making, yet their outputs may be inscrutable: predictions that humans cannot readily understand or explain. This tension conflicts with administrative law’s demand for reason-giving to respect individuals’ agency. The article develops two linked claims: adequate reasons are crucial to agency, and incorporating inscrutable algorithmic predictions undermines this ideal. It further argues that sustained reliance on such tools can produce systemic “re-enchantment,” shrinking the humanly explainable in public life and altering how people experience the administrative state.","Modern Law Review  \nDOI: 10.1111/1468-2230.12843  \nMachine Learning and the Re-Enchantment of the Administrative State  \nEden Sarid∗  and Omri Ben-Zvi†  \nMachine 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.  \nINTRODUCTION  \nIn recent years, the use of machine learning algorithms by administrative agencies has sparked considerable debate in public law scholarship.1 On the one hand, these algorithms are generally considered to be able to outperform standard human cognition in managing certain tasks, such as analysing large amounts of data and generating meaningful correlations, distinctions, and predictions,suggesting real promise for the administrative state 2 On the other hand, machine learning algorithms introduce new problems and concerns for public law and raise novel issues of regulatory design. For example, some of these  \n∗ Essex Law School.  \n† Hebrew University ofJerusalem, Faculty of Law. We thank Haim Abraham, David Dyzenhaus, Aviv Gaon, Lital Helman, Aziz Huq, Ariel Katz, Mary Mitchell, Daragh Murray, Simon Stern, Oren Tamir, and the MLR reviewers for their helpful comments. Earlier drafts of this article were presented atthe Federmann Cyber Security Research Center Cyberlaw Workshop, ICON-S annual conference, CUHK Machine Lawyering Conference, and University of Toronto Innovation Law Workshop. We thank the participants in these workshops for their valuable input.  \n1 See Aziz Huq,‘A Right to a Human Decision’(2020) 106 Virginia Law Review 611, 618; Cary Coglianese,‘Administrative Law in the Automated State’ (2021) 150 Daedalus 104; Rebecca Williams,‘Rethinking Administrative Law for Algorithmic Decision Making’(2022) 42 OJLS 468; Jennifer Cobbe, ‘Administrative Law and The Machines of Government: Judicial Review of Automated Public-Sector Decision-Making’(2019) 39 Legal Studies 636.  \n2 See Coglianese, ibid; Williams, ibid. See also Katherine Strandburg,‘Rulemaking and Inscrutable Automated Decision Tools’(2019) 119 Columbia Law Review 1851, 1857 .  \n© 2023 The Authors. The Modern Law Review published by John Wiley & Sons Ltd on behalf of Modern Law Review Limited.  \n(2023)00(0)MLR1–27  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nMachine Learning and Administrative State  \nalgorithms are not transparent and they can be ‘gamed’ by external parties or influenced by various biases.3  \nA key concern on which this article focuses is the issue ofinscrutability. Some machine learning algorithms employ advanced techniques that generate predictions and recommendations that are inscrutable in the sense that humans cannot understand the reasoning behind them.4 Inscrutability is the result of several factors. First, to generate any specific output, these algorithms analyse enormous amounts of informat","cbCaimotM33VZaYg","https://ap.wps.com/l/cbCaimotM33VZaYg","pdf",276550,1,27,"English","en",105,"# Introduction\n## Machine learning in administrative agencies\n## The problem of inscrutability and the black box challenge","[{\"question\":\"Why do inscrutable machine learning algorithms challenge administrative law?\",\"answer\":\"Agencies may lack access to the data and logical processes behind algorithmic predictions, conflicting with administrative law’s emphasis on providing reasons. This black box problem is central to the article’s concern.\"},{\"question\":\"How does reason-giving relate to respecting individuals’ agency?\",\"answer\":\"The article argues that giving adequate reasons is a significant facet of respecting individuals’ agency. When inscrutable algorithmic predictions enter administrative decisions, this normative ideal is compromised.\"},{\"question\":\"What is “re-enchantment” in this context?\",\"answer\":\"“Re-enchantment” refers to systemic effects that gradually reduce the realm of the humanly explainable in public life. The article suggests that inscrutable machine learning may trigger a special form of re-enchantment, changing how people understand the administrative state and experience public life.\"}]","Machine Learning and the Re-Enchantment of the Administrative State - Article | PDF",1785720581,68,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-the-re-enchantment-of-the-administrative-state-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-the-re-enchantment-of-the-administrative-state-article/118844/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why do inscrutable machine learning algorithms challenge administrative law?","Question",{"text":75,"@type":76},"Agencies may lack access to the data and logical processes behind algorithmic predictions, conflicting with administrative law’s emphasis on providing reasons. 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