[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117112-en":3,"doc-seo-117112-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117112,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Reliability in Machine Learning - Article","Issues of reliability occupy a central place in the epistemology of machine learning. The paper integrates multiple strands of existing scholarship and identifies promising avenues for future research, while also giving an accessible introduction to core ideas in statistics and machine learning that relate specifically to reliability. It addresses how reliability threats arise across model development, deployment, and adaptation of socio-technical environments.","Received: 13 December 2023  \nRevised: 12 March 2024  \nAccepted: 16 April 2024  \nDOI: 10. 1111/phc3.12974  \nARTICLE   \nReliability in Machine Learning  \nThomas Grote1  | Konstantin Genin1 | Emily Sullivan2  \n1Cluster of Excellence: Machine Learning: New Perspectives for Science, University of Tübingen, Tübingen, Germany 2Department of Theoretical Philosophy, Utrecht University, Utrecht, The Netherlands  \nCorrespondence  \nThomas Grote, Cluster of Excellence: Machine Learning: New Perspectives for Science, University of Tübingen, Maria von Linden Str. 6, D‐72076 Tübingen, Germany.  \nEmail: [thomas.grote@uni-tuebingen.de](thomas.grote@uni-tuebingen.de)  \nFunding information  \nDeutsche Forschungsgemeinschaft, Grant/ Award Number: BE5601/4‐1; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Number: VI. Veni. 201F.051; Carl‐Zeiss‐Stiftung  \nAbstract  \nIssues of reliability are claiming center‐stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.  \n1 | INTRODUCTION  \nMachine learning models often achieve impressive accuracy under training conditions, but fail in spectacular or unexpected ways when they are deployed in real‐world settings. Is there some way to guarantee that predictive accuracy in training carries over to the settings in which models are actually deployed? In other words: can we be justified in relying on machine learning models on the basis of their performance in training?  \nThe underlying challenge is that there are various threats to reliability, arising at the time of (i) model development,(ii) model deployment and (iii) adapting the socio‐technical environment to accommodate the model. Concerning (i), unlike traditional statistical models, there is no widely accepted mathematical theory that explains why and when state‐of‐the‐art models such as deep neural networks generalize well. As for (ii), machine learning models are commonly used in unstable environments, or even induce changes to the environment itself, and they can be fooled by humanly imperceptible manipulations to the data. Regarding (iii), one basic issue is to align the model output with the existing epistemic norms in a given domain, which often involves aggregating the model output with other kinds of evidence. In addition, there is another overarching problem: machine learning models are  \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.  \n© 2024 The Authors. Philosophy Compass published by John Wiley & Sons Ltd.  \nPhilosophy Compass. 2024;e12974.  \n[https://doi.org/10.1111/phc3.12974](https://doi.org/10.1111/phc3.12974)  \nwi[leyonlinelibrary.com/journal/phc3](leyonlinelibrary.com/journal/phc3)  \n1 of 11  \n2 of 11 -  GROTE ET AL.  \nnotoriously opaque, which is why it is difficult to understand the inner logic of how and why they arrive at a given prediction.  \nWhat is the relationship between the individual threats to reliability? When evaluating machine learning models, what types of assurances should we prioritize? What does the alleged (un)reliability tell us about the epistemic status of machine learning models in science and society? Can failure cases in machine learning even animate conceptual expansions or revisions of existing epistemological theories? These questions, among others, claim center‐stage in the emerging debate on reliability in machine learning, spanning across statistical learning theory, mainstream epistemology, and the modeling literature in philosophy of science. This paper unifies different branches in the epistemology of machine learning and points to promising research directions, whilst also providing an access","cbCaisoVYd3t8DpE","https://ap.wps.com/l/cbCaisoVYd3t8DpE","pdf",262253,1,11,"English","en",105,"# Introduction\n## Statistical learning theory: the received view","[{\"question\":\"Why can training accuracy fail in real-world deployment?\",\"answer\":\"Machine learning models may behave unexpectedly when moved from training to real-world conditions. The paper emphasizes threats to reliability that appear during development, deployment, and socio-technical adaptation.\"},{\"question\":\"What kinds of threats to reliability does the paper distinguish?\",\"answer\":\"It groups reliability threats into those arising during model development, model deployment, and adapting the socio-technical environment. Each stage involves different sources of unreliability, including limited theories for generalization and susceptibility to subtle data manipulations.\"},{\"question\":\"What is the role of statistical learning theory in reliability?\",\"answer\":\"Statistical learning theory is presented as the native reliability theory for machine learning. It treats reliability in an “ideal case” where threats are well understood and can be managed precisely.\"}]","Reliability in Machine Learning - Article | PDF",1785673908,28,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"reliability-in-machine-learning-article","",{"@graph":36,"@context":86},[37,54,69],{"@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/reliability-in-machine-learning-article/117112/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why can training accuracy fail in real-world deployment?","Question",{"text":76,"@type":77},"Machine learning models may behave unexpectedly when moved from training to real-world conditions. The paper emphasizes threats to reliability that appear during development, deployment, and socio-technical adaptation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What kinds of threats to reliability does the paper distinguish?",{"text":81,"@type":77},"It groups reliability threats into those arising during model development, model deployment, and adapting the socio-technical environment. Each stage involves different sources of unreliability, including limited theories for generalization and susceptibility to subtle data manipulations.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the role of statistical learning theory in reliability?",{"text":85,"@type":77},"Statistical learning theory is presented as the native reliability theory for machine learning. It treats reliability in an “ideal case” where threats are well understood and can be managed precisely.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]