[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117617-en":3,"doc-seo-117617-105":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},117617,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Fixing Foundational Concepts in Machine Learning - A Methodological Primer","Many foundational concepts in machine learning face sustained criticism for failing to capture essential functional roles. Philosophers use conceptual engineering to systematize this critique, and the work provides theoretical and methodological grounding for future engineering efforts. It analyzes functional roles of concepts, diagnoses underlying causes and deficiency types, and proposes criteria for successful propagation of reengineered concepts within and beyond the machine learning community, while addressing limits imposed by operationalization and tensions between sociopolitical aims and computational feasibility.","Fixing Foundational Concepts in Machine Learning: A Methodological Primer1  \nName: Thomas Grote (corresponding)  \nEmail: [thomas.grote@utn.de](thomas.grote@utn.de)  \nOrcid: [https://orcid.org/0000-0002-9832-6046](https://orcid.org/0000-0002-9832-6046)  \nAffiliation: University of Technology Nuremberg; Department of Computer Science and Artificial Intelligence; Dr.-Luise-Herzberg-Str. 4, 90461 Nuremberg, Germany.  \nName: Alice C.W. Huang  \nEmail: [alice.huang@uwo.ca](alice.huang@uwo.ca)  \nOrcid: 0000-0002-1719-1945 Affiliations:  \nWestern University Department of Philosophy and Department of Computer Science  \n1151 Richmond Street, London, ON, N6A 3K7, Canada. Schwartz Reisman Institute for Technology and Society  \n108 College St, Toronto, ON M5G 0C6, Canada.  \nAbstract: Many foundational concepts in machine learning have been criticized as inadequate. Philosophers have therefore taken it upon themselves to sort out the conceptual terrain—with conceptual engineering being the method of choice. This paper takes a step back to provide theoretical and methodological grounding for future work on conceptual engineering in machine learning. To this end, we consider the functional roles of concepts in machine learning, the underlying causes and types of deficiency, and map out criteria for the successful propagation of reengineered concepts within and beyond the machine learning community. Moreover, we discuss how the space of viable conceptual revisions in machine learning is constrained by the need foroperationalization, and how tensions can occur between the sociopolitical desirability and the computational implementability of relevant conceptual engineering projects. Overall, our goal is to delineate how conceptual work in philosophy ought to be if the goal is for our contribution to permeate through the science and practice of machine learning.  \nKeywords: Machine Learning; Conceptual Engineering; Methods in Philosophy; Explication; Artificial Intelligence;  \n1 Joint first authorship  \n1. Introduction  \nMany concepts in machine learning have been criticized as inadequate. For example, the‘bias-variance trade-off’, a tenet of standard machine learning theory, says that as a model becomes more complex to fit the training data better, its ability to generalize to new data often decreases, and vice versa. It is, however, unable to explain why deep neural networks perform so well (Zhang et al., 2021; Belkin et al., 2019) . For ‘fairness’, a menagerie of different statistical criteria has been proposed, many of which are mutually incompatible (Kleinberg et al., 2016) . The notion of‘interpretability’, which broadly refers to how easily humans can understand a model, is ill-defined (Lipton, 2018) . Moreover, the advent of large language models has drastically increased the nonchalant use of human-centric concepts from cognitive psychology—such as ‘general intelligence’and ‘theory of mind’ – to assess model behavior (Bubeck et al., 2023; Binz & Schulz, 2023) .  \nAgainst this backdrop, it is hardly surprising that many philosophers have taken it upon themselves to sort out the conceptual repertoire in machine learning. One promising avenue is conceptual engineering: In broad strokes, it presents a revisionary approach to fixing concepts. Rather than looking for a definition that tracks the extensions of a concept, the starting point is to consider what function said concept ought to play in a given (social, scientific, or philosophical) practice and then modify the concept so that it fulfills this function more adequately (Cappelen, 2018; Haslanger, 2012) . Among others, these functions can be combating gender injustice (Haslanger, 2012; Jenkins, 2016), achieving scientific exactness (Carnap, 1950), or resolving logical paradoxes (Sharp, 2013) . For instance, in Haslanger’s (2000) seminal paper, she argues that, to understand race and gender, instead of studying what the words ‘gender’ and ‘race’ refer to in ordinary discourse, we should ","cbCais4O6gOOnLpL","https://ap.wps.com/l/cbCais4O6gOOnLpL","pdf",377463,1,31,"English","en",105,"# Introduction\n## Conceptual engineering as a revisionary approach\n## Examples in machine learning\n## Methodological focus and research questions","[{\"question\":\"Why are foundational concepts in machine learning considered inadequate?\",\"answer\":\"Multiple concepts are criticized for being ill-defined or unable to explain empirical behavior, and for fairness criteria that can be mutually incompatible. The paper motivates these problems as a reason for conceptual engineering.\"},{\"question\":\"What is conceptual engineering in machine learning?\",\"answer\":\"It starts from the function a concept should play in a given practice rather than from definitions that track extensions. Concepts are revised so they fulfill these functions more adequately.\"},{\"question\":\"How does the paper evaluate whether reengineered concepts can propagate successfully?\",\"answer\":\"It maps out criteria for successful propagation both within and beyond the machine learning community, while noting constraints from the need for operationalization.\"}]","Fixing Foundational Concepts in Machine Learning - A Methodological Primer | PDF",1785677299,78,{"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},"fixing-foundational-concepts-in-machine-learning-a-methodological-primer","",{"@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/fixing-foundational-concepts-in-machine-learning-a-methodological-primer/117617/",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","2026-08-02",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 are foundational concepts in machine learning considered inadequate?","Question",{"text":75,"@type":76},"Multiple concepts are criticized for being ill-defined or unable to explain empirical behavior, and for fairness criteria that can be mutually incompatible. The paper motivates these problems as a reason for conceptual engineering.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is conceptual engineering in machine learning?",{"text":80,"@type":76},"It starts from the function a concept should play in a given practice rather than from definitions that track extensions. Concepts are revised so they fulfill these functions more adequately.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the paper evaluate whether reengineered concepts can propagate successfully?",{"text":84,"@type":76},"It maps out criteria for successful propagation both within and beyond the machine learning community, while noting constraints from the need for operationalization.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]