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The paper brings together multiple strands of research and clarifies connections between reliability concerns across model development, deployment, and adaptation of socio-technical environments. It also serves as an accessible introduction to reliability-related concepts in statistics and machine learning, while outlining research directions and key questions about assurances, failure cases, and epistemic status in science and society.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/reliability-in-machine-learning/290231/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/reliability-in-machine-learning/290231.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is reliability central in machine learning epistemology?","Question",{"text":113,"@type":114},"Reliability concerns play a central role because models can fail in unexpected ways outside training, raising questions about whether training performance justifies real-world reliance.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What are the main sources of threats to reliability?",{"text":118,"@type":114},"Threats arise during model development, model deployment, and adapting the socio-technical environment, including generalization challenges, unstable or adversarial environments, and alignment with domain epistemic norms.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the paper relate statistical learning theory to deep learning successes?",{"text":122,"@type":114},"It highlights that deep learning’s empirical successes since the 2010s seem incongruous with statistical learning theory’s theoretical predictions, motivating efforts to reconcile the two.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290231,1789641023,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":41,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","Edinburgh Research Explorer  \nReliability in machine learning  \nCitation for published version:  \nGrote, T, Genin, K & Sullivan, E 2024, 'Reliability in machine learning', Philosophy Compass, vol. 19, no. 5, e12974 . [https://doi.org/10.1111/phc3.12974](https://doi.org/10.1111/phc3.12974)  \nDigital Object Identifier (DOI):  \n10.1111/phc3.12974  \nLink:  \nLink to publication record in Edinburgh Research Explorer  \nDocument Version:  \nPublisher's PDF, also known as Version of record  \nPublished In:  \nPhilosophy Compass  \nGeneral rights  \nCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please [contact openaccess@ed.ac.uk](contact openaccess@ed.ac.uk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 27. Jul. 2026  \nReceived: 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 ori","cbCaivIVXqEzJBLg","https://ap.wps.com/l/cbCaivIVXqEzJBLg","pdf",385992,"English","# Introduction\n## Statistical Learning Theory: The Received View","[{\"question\":\"Why is reliability central in machine learning epistemology?\",\"answer\":\"Reliability concerns play a central role because models can fail in unexpected ways outside training, raising questions about whether training performance justifies real-world reliance.\"},{\"question\":\"What are the main sources of threats to reliability?\",\"answer\":\"Threats arise during model development, model deployment, and adapting the socio-technical environment, including generalization challenges, unstable or adversarial environments, and alignment with domain epistemic norms.\"},{\"question\":\"How does the paper relate statistical learning theory to deep learning successes?\",\"answer\":\"It highlights that deep learning’s empirical successes since the 2010s seem incongruous with statistical learning theory’s theoretical predictions, motivating efforts to reconcile the two.\"}]","Reliability in machine learning | PDF"]