Philosophy of Science for Machine Learning: Core Issues and New Perspectives

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.

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.