[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127828-en":3,"doc-seo-127828-105":31,"detail-sidebar-cat-0-en-105":75},{"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":11,"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":28,"update_tm":29,"read_time":30},127828,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","MACHINE LEARNING AND THEORY-LADENNESS","\u003Cp>Machine learning methods in scientific research have revived debates on theory-ladenness: whether and how machine learning models and modelling strategies are shaped by the domain theory of the field where they are applied. The article argues against oversimplified extremes that ML-assisted science is either no different from pre-ML science or fully theory-free. By comparing machine learning models with phenomenological models, it analyzes roles of domain-theory across construction, implementation, and interpretation. It also proposes categories of theory-ladenness—theory indifference, theory-aid, and theory-infection—to clarify varying degrees of influence and their implications for expertise, curricula, and scientific practice.\u003C/p>","\u003Cp>MACHINE LEARNING AND THEORY-LADENNESS: A  \u003C/p>\u003Cp>PHENOMENOLOGICAL ACCOUNT  \u003C/p>\u003Cp>Alberto Termine, IDSIA USI-SUPSI, Lugano  \u003C/p>\u003Cp>Emanuele Ratti 1, University of Bristol  \u003C/p>\u003Cp>Alessandro Facchini, IDSIA USI-SUPSI, Lugano  \u003C/p>\u003Cp>Abstract. In recent years, the dissemination of machine learning (ML) methodologies in scientific research has prompted discussions on theory-ladenness. More specifically, the issue of theory-ladenness has re-emerged as questions about whether and how ML models (MLMs) and ML modelling strategies are impacted by the domain theory of the scientific field in which ML is used and implemented (e.g., physics, chemistry, biology, etc) . On the one hand, some have argued that there is no difference between ‘traditional’ (pre-ML) and ML-assisted science. In both cases, theory plays an essential and unavoidable role in the analysis of phenomena and the construction and use of models. Others have argued instead that ML methodologies and models are theory-independent and, in some cases, even theory-free. In this article, we argue that both positions are overly simplistic and do not advance our understanding of the interplay between ML methods and domain theories. Specifically, we provide an analysis of theory-ladenness in ML-assisted science. We do so by constructing an account of MLMs based on a comparison with phenomenological models (PMs), and we show that seeing MLMs through the lens of the debate on PMs, can shed light on the subtle roles that domain-theory plays in the various steps of the construction and use of MLMs. Our analysis reveals that, while the construction of MLMs can be relatively independent of domain-theory, the practical implementation and interpretation of these models within a given specific domain still relies on fundamental theoretical assumptions and background knowledge. Based on our analysis, we introduce new categories of theory-ladenness- 'theory indifference,' 'theory-aid,' and 'theory-infection’ -to capture the varying degrees of influence of domain-theory on MLMs. This analysis of theory-ladenness has far-reaching consequences for understanding the role of ML practices in contemporary science, and the relation between ML specialists and scientists of a given field.  \u003C/p>\u003Cp>1. Introduction  \u003C/p>\u003Cp>Philosophers of science have discussed the role of human expertise in building and implementing machine learning models (MLM) in science2. There is a consensus that some kind of human expertise is always required in building and using machine learning (ML) methods (see e.g. Hansen and Quinon 2023) . However, underneath this agreement, it remains unclear what kind of human expertise is needed in the context of ML, whether scientific and/or purely engineering-based. For this reason, there is much confusion on the extent to which ML methods are theory-laden, where by ‘theory’ here we refer to the domain knowledge of the given scientific field in which a ML method is implemented (e.g. physics, biology, etc) . In the philosophical debate, some emphasize the a-theoretical nature of ML methods (Napoletani et al 2022; Pietsch 2015), while others have shown that there are significant aspects of ML requiring domain knowledge expertise (Ratti 2020; Hansen and Quinon 2023) . This issue is  \u003C/p>\u003Cp>1 Corresponding author, [mnl.ratti@gmail.com](mnl.ratti@gmail.com)  \u003C/p>\u003Cp>2 Our focus is only in the use of machine learning in the sciences, in particular in the natural and life sciences.  \u003C/p>\u003Cp>among the most relevant ones in the epistemology of ML-based science. Identifying the extent to which ML methods are theory-laden and need domain knowledge expertise to be built and used, has repercussions on the kind of expertise needed for ML-based science (whether purely engineering-based, or else), the way curricula for training future ML-expert in the sciences should be shaped, and whether ML, as a practice, is a tool for the unification of the sciences beyond disciplinary and theoretical differences.  \u003C/p>\u003Cp>The aim of this article is to shed light on the nature of the\u003C/p>","cbCaiilQSM1TGXrG","https://ap.wps.com/l/cbCaiilQSM1TGXrG","pdf",479687,1,29,"English","en",105,"# Introduction\n## Machine learning, blind methods, and theory-ladenness\n# Overview of the argument","","MACHINE LEARNING AND THEORY-LADENNESS | PDF","Machine learning methods in scientific research have revived debates on theory-ladenness: whether and how machine learning models and modelling strategies are shaped by the domain theory of the field where they are applied. The article argues against oversimplified extremes that ML-assisted science is either no different from pre-ML science or fully theory-free. By comparing machine learning models with phenomenological models, it analyzes roles of domain-theory across construction, implementation, and interpretation. It also proposes categories of theory-ladenness—theory indifference, theory-aid, and theory-infection—to clarify varying degrees of influence and their implications for expertise, curricula, and scientific practice.",1787314003,73,{"code":4,"msg":32,"data":33},"ok",{"site_id":24,"language":23,"slug":34,"title":27,"keywords":26,"description":28,"schema_data":35,"social_meta":70,"head_meta":72,"extra_data":74,"updated_unix":29},"machine-learning-and-theory-ladenness-a-phenomenological-account-127828",{"@graph":36,"@context":69},[37,54],{"@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":27,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-theory-ladenness-a-phenomenological-account-127828/127828/",4,{"url":52,"name":27,"@type":55,"author":56,"headline":27,"publisher":58,"fileFormat":61,"inLanguage":23,"description":28,"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","2026-09-03","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":11},"InteractionCounter",{"@type":68},"ViewAction","https://schema.org",{"og:url":52,"og:type":71,"og:title":27,"og:site_name":59,"og:description":28},"article",{"robots":73,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":76},[77,81,85,89,94,99,104,107,112,115,119],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":78,"show_sort_weight":79,"slug":80},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":82,"show_sort_weight":83,"slug":84},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Exam",70,"exam",{"id":90,"doc_module":4,"doc_module_name":46,"category_name":91,"show_sort_weight":92,"slug":93},5,"Comic",60,"comic",{"id":95,"doc_module":4,"doc_module_name":46,"category_name":96,"show_sort_weight":97,"slug":98},6,"Technology",50,"technology",{"id":100,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":105,"slug":106},30,"research-report",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},9,"Religion & Spirituality",20,"religion-spirituality",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":110,"slug":114},"World Cup","world-cup",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":116,"slug":118},10,"Lifestyle","lifestyle",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":90,"slug":122},19,"General","general"]