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Philosophy of Science for Machine Learning
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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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Keywords
- Computational methodologies
- Epistemology
- Machine learning
- open access
- philosophy of machine learning
- philosophy of science
- Philosophy of Technology
- Scientific Practice with Machine Learning
- thema EDItEUR::P Mathematics and Science::PD Science: general issues::PDA Philosophy of science
- thema EDItEUR::Q Philosophy and Religion::QD Philosophy
- 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
Links
DOI: 10.1007/978-3-032-03083-2Editions