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Philosophy of Science for Machine Learning

This open access book offers a comprehensive and systematic debate on the key concepts and areas of application of the philosophy of science for machine learning. The current landscape of the debate about the epistemic and methodological challenges raised by machine learning in scientific fields is...

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Guardado en:
Formato: Online
Lenguaje:inglés
Publicado: Springer Nature 2025
Materias:
Open Access
Philosophy of science
Epistemology
Machine Learning
Computational methodologies
Scientific Practice with Machine Learning
philosophy of machine learning
philosophy of technology
thema EDItEUR::P Mathematics and Science::PD Science: general issues::PDA Philosophy of science
thema EDItEUR::Q Philosophy and Religion::QD Philosophy::QDT Topics in philosophy::QDTK Philosophy: epistemology and theory of knowledge
thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence
thema EDItEUR::Q Philosophy and Religion::QD Philosophy
thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues
Acceso en línea:ONIX_20251218T100927_9783032030832_55
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Sumario:This open access book offers a comprehensive and systematic debate on the key concepts and areas of application of the philosophy of science for machine learning. The current landscape of the debate about the epistemic and methodological challenges raised by machine learning in scientific fields is fragmented and lacks a common thread that helps to understand the complexity of the issue. Against this background, this book brings together expert researchers in the field, structuring the debate in ways that allow readers to navigate quickly in this evolving field of research and pave the way to new paths of philosophical and technical research. Although the book is written from the perspective of philosophy of science and epistemology, it is of interest to philosophers in a myriad of fields, such as philosophy of mind, philosophy of language, philosophy of neuroscience, and metaphysics of science, STS studies, as well as to researchers working on technical and computational issues such as explainability, trustworthiness, interpretability, transparency.

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