Philosophy of Science for Machine Learning: Core Issues and New Perspectives (Synthese Library, 527) - Hardcover

Hardcover

ISBN 10: 303203082X ISBN 13: 9783032030825

Publisher: Springer, 2025

Synopsis

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.

About this title

About the Author

Juan M. Durán is an Assistant Professor at the Delft University of Technology. His work has focused on the intersection between philosophy of science and technology, first with computer simulations and more recently with machine learning. He also has extensive work on the ethics of technology. In 2019 he was awarded the Herbert A. Simon Award for outstanding research in computing and philosophy. This award is offered by the International Association for Computing and Philosophy (IACAP) and recognizes scholars at an early stage of their academic career who are likely to reshape debates at the nexus of computing and philosophy with their original research. He has held visiting fellowships at the University of Virginia, Tilburg University, and the Netherlands Institute for Advanced Studies in the Humanities and Social Sciences. His current work focuses on developing Computational Reliabilism, a theoretical framework for the justification of our belief in the output of machine learning.

 

Giorgia Pozzi is a Ph.D. Candidate at TU Delft working at the intersection between the ethics and epistemology of explanatory AI, with a particular interest in machine-learning implementations in the field of medicine and healthcare. Among others, her research focuses on injustices that can emerge in connection to ML in healthcare, particularly due to the epistemic limitations of ML systems. Furthermore, she is interested in questions regarding the bearing of epistemic justification on moral justification in AI-based medical decision-making. She is thus interested in making explicit and investigating in-depth the conflating and intertwined nature of epistemology and ethics in the context of AI. Before joining TU Delft, she obtained a Bachelor's degree in Philosophy (focusing on moral philosophy and metaethics) and in Chinese Studies at Ludwig-Maximilians-University (LMU) in Munich. Afterwards, she completed her Master’s focusing on the ethics and epistemology of artificial intelligence at the same university.

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2025
• Language: English
• ISBN 10: 303203082X
• ISBN 13: 9783032030825
• Binding: Hardcover
• Number of pages: 529
• Editor: Durán Juan M., Pozzi Giorgia