[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117762-en":3,"doc-seo-117762-105":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},117762,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","The importance of expert knowledge in big data and machine learning","Big data and machine learning are often portrayed as a fully novel scientific method that can replace human input and even bring about the “end of theory.” The argument in favor claims that advanced algorithms can extract solutions from large data sets without prior knowledge and that correlation is enough. The article challenges this view, using two case studies—skin cancer detection and protein folding—to show that expert knowledge is indispensable and embedded in practical application. It classifies expert knowledge types, examines consequences for scientific practice, and argues for ongoing conceptual shifts rather than a rigid paradigm break.","Roskilde University  \nThe importance of expert knowledge in big data and machine learning  \nHansen, Jens Ulrik; Quinon, Paula  \nPublished in: Synthese  \nDOI:  \n10.1007/s11229-023-04041-5  \nPublication date:  \n2023  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (APA):  \nHansen, J. U. , & Quinon, P. (2023) . The importance of expert knowledge in big data and machine learning. Synthese, 201(35),[35] . [https://doi.org/10.1007/s11229-023-04041-5](https://doi.org/10.1007/s11229-023-04041-5)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain.  \n• You may freely distribute the URL identifying the publication in the public portal.  \nTake down policy  \nIf you believe that this document breaches copyright please contact [rucforsk@kb.dk](rucforsk@kb.dk) providing details, and we will remove access to the work immediately and investigate your claim.  \nDownload date: 09. Feb. 2023  \nSynthese (2023) 201:35  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1229-023-04041-5  \nORIGINAL RESEARCH  \nThe importance of expert knowledge in big data and machine learning  \nJens Ulrik Hansen1 · Paula Quinon2  \nReceived: 8 August 2022 / Accepted: 1 January 2023 © The Author(s) 2023  \nAbstract  \nAccording to popular belief, big data and machine learning provide a wholly novel approach to science that has the potential to revolutionise scientific progress and will ultimately lead to the ‘end of theory’. Proponents of this view argue that advanced algorithms are able to mine vast amounts of data relating to a given problem without any prior knowledge and that we do not need to concern ourselves with causality, as correlation is sufficient for handling complex issues. Consequently, the human contribution to scientific progress is deemed to be non-essential and replaceable. We, however, following the position most commonly represented in the philosophy of science, argue that the need for human expertise remains. Based on an analysis of big data and machine learning methods in two case studies—skin cancer detection and protein folding—we show that expert knowledge is essential and inherent in the application of these methods. Drawing on this analysis, we establish a classification of the different kinds of expert knowledge that are involved in the application of big data and machine learning in scientific contexts. We address the ramifications of a human-driven expert knowledge approach to big data and machine learning for scientific practice and the discussion about the role of theory. Finally, we show that the ways in which big data and machine learning both influence and are influenced by scientific methodology involve continuous conceptual shifts rather than a rigid paradigm change.  \nKeywords Big data · Machine learning · Expert knowledge · Agnostic sciences · Inductive method · Role of theory · Paradigm shift  \nThe authors are listed in alphabetical order.  \nB Jens Ulrik Hansen [jensuh@ruc.dk](jensuh@ruc.dk)  \nB Paula Quinon [paula.quinon@pw.edu.pl](paula.quinon@pw.edu.pl)  \n1 Department of People and Technology, Roskilde University, Roskilde, Denmark  \n2 Faculty of Administration and Social Sciences, Warsaw University of Technology, Warsaw, Poland  \n1 3  \n1 Introduction  \nAccording to popular belief, big data and machine learning provide a wholly novel approach to science that could potentially revolutionise scientific progress (Hey et al., 2009a; Kitchin, 2014) . A radical expression of this belief can be found in Chris Ande","cbCaiqUBvEaLxjSp","https://ap.wps.com/l/cbCaiqUBvEaLxjSp","pdf",340647,1,22,"English","en",105,"# Introduction\n## Big data and the “end of theory” view\n## The “Fourth Paradigm” and eScience","[{\"question\":\"What claim does the paper address about big data and machine learning and the “end of theory”?\",\"answer\":\"It addresses the belief that big data and machine learning enable scientific progress without prior knowledge, making theory and causal explanation unnecessary, summarized as the “end of theory.”\"},{\"question\":\"How do the two case studies support the paper’s main argument?\",\"answer\":\"Using skin cancer detection and protein folding, the paper shows that expert knowledge is essential and inherent when applying big data and machine learning in scientific contexts.\"},{\"question\":\"What does the paper conclude about expert knowledge and scientific change?\",\"answer\":\"It develops a classification of different kinds of expert knowledge involved and argues that big data and machine learning drive continuous conceptual shifts rather than a strict paradigm change.\"}]","The importance of expert knowledge in big data and machine learning | 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claim does the paper address about big data and machine learning and the “end of theory”?","Question",{"text":75,"@type":76},"It addresses the belief that big data and machine learning enable scientific progress without prior knowledge, making theory and causal explanation unnecessary, summarized as the “end of theory.”","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do the two case studies support the paper’s main argument?",{"text":80,"@type":76},"Using skin cancer detection and protein folding, the paper shows that expert knowledge is essential and inherent when applying big data and machine learning in scientific contexts.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the paper conclude about expert knowledge and scientific change?",{"text":84,"@type":76},"It develops a classification of different kinds of expert knowledge involved and argues that big data and machine learning drive continuous conceptual shifts rather than a strict paradigm 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