[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121598-en":3,"doc-seo-121598-105":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},121598,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Machine Learning and Theory-Ladenness - A Phenomenological Account","The analysis investigates theory-ladenness in machine learning within the sciences, where “theory” is treated as domain knowledge of the discipline implementing ML. By modeling machine learning models through comparison with phenomenological models, the argument claims that ML model-building is largely indifferent to domain-theory. The resulting thesis carries consequences for cross-disciplinary transferability and shifts the debate from descriptive diagnosis toward normative priorities for theory-ladenness in ML.","Draft. Comments are welcome.  \nMACHINE LEARNING AND THEORY-LADENNESS: A  \nPHENOMENOLOGICAL ACCOUNT  \nAlberto Termine, IDSIA USI-SUPSI, Lugano  \nEmanuele Ratti 1, University of Bristol  \nAlessandro Facchini, IDSIA USI-SUPSI, Lugano  \nAbstract. We provide an analysis of theory-ladenness in machine learning (ML) in science, where‘theory’ (that we call 'domain-theory') refers to the domain knowledge of the scientific discipline where ML is used. By constructing an account of ML models based on a comparison with phenomenological models, we show (against recent trends in philosophy of science) that ML model-building is mostly indifferent to domain-theory. This claim, we argue, has far-reaching consequences for the transferability of ML across scientific disciplines, and shifts the priorities of the debate on theoryladenness in ML from descriptive to normative.  \n1. INTRODUCTION  \nThe development of data-intensive methods in the sciences (from Big Data, data science, to AI) has been often associated with the idea that these can function without inputs from scientific expertise, or without appealing to domain knowledge of the scientific fields in which they are used. This idea has its origin in the debate on the so-called ‘two cultures of statistical modeling’(Breiman 2001), where ‘predictive modeling’(what later became Big Data, data science, machine learning, and then contemporary AI) is characterized by a level of independence from considerations coming from the domain of implementation which is just absent in more ‘traditional’ statistical modeling practices (Shmueli 2010) . Such independence has been even popularized as a new scientific paradigm which came to be known, infamously, as the ‘End of Theory’(Anderson 2008) .  \nGauging whether data-intensive methods are independent from theoretical considerations coming from the scientific domain of implementation means asking a question about the theory-ladenness of data-intensive methods. By ‘theory-ladenness’, we mean here the idea that, in data-intensive science, engaging in essential activities of scientific practice requires either the implicit assumption of, or the explicit appeal to, scientific theories. We understand the term‘theory’ in a broad sense (specified in more detail below) to include domain knowledge and expertise of the given scientific field in which data-intensive methods  \n1 Corresponding author, [mnl.ratti@gmail.com](mnl.ratti@gmail.com)  \nare used. While in data-intensive science - and especially in machine learning (ML) -there might be ideas coming from statistical learning theory that can reasonably count as ‘theory’, here theory-ladenness is restricted to the ‘domain-theory’ belonging to the scientific context of implementation of the model.  \nWith few exceptions (Napoletani et al. 2020), philosophers of science have been systematically arguing in favor of theory-ladenness (Callebaut 2012; Kitchin 2014; Leonelli 2016; Boon 2020; Knusel and Baumberger 2020; Hansen and Quinon 2023), by pointing to its inevitability and showing the subtle ways in which theoretical considerations (broadly conceived) inform the construction and use of these data-intensive methods.  \nThe aim of this article is to argue against the inevitability of theory-ladenness when applied to the specific case of ML. In particular, we argue against this inevitability in the case of practices related to the construction of ML models (MLM), and we point to neglected consequences that arguments in this context might lead to. Asking the question of theoryladenness (and arguing against the inevitability of theory-ladenness) has a number of implications, as we will show. If ML-based data-intensive methods are indeed theoryindependent, then data scientists/AI practitioners do not need much information coming from scientific expertise, and they can potentially transfer their tools and methods across a number of scientific contexts seamlessly. If data-intensive science based on, say, cutting-edge AI me","cbCaicg5BnPgZr29","https://ap.wps.com/l/cbCaicg5BnPgZr29","pdf",551468,1,33,"English","en",105,"# Introduction\n## Theory-ladenness in data-intensive science\n## Aim and implications of the article\n## Article structure and key claims","[{\"question\":\"What does the article mean by “theory” and “domain-theory” in machine learning?\",\"answer\":\"“Theory” is understood broadly, but theory-ladenness is restricted here to “domain-theory,” meaning the domain knowledge and expertise of the scientific field where ML models are implemented.\"},{\"question\":\"What is the central claim about the role of domain-theory in ML model-building?\",\"answer\":\"ML model-building is argued to be mostly indifferent to domain-theory, based on an account that compares machine learning models with phenomenological models.\"},{\"question\":\"How does the argument affect transferability of ML across scientific disciplines?\",\"answer\":\"If ML practices are largely theory-independent at the model-construction level, ML tools and methods may transfer more seamlessly across disciplines, and priorities in training and debate should be reconsidered accordingly.\"}]","Machine Learning and Theory-Ladenness - 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