Skip to main content
Springer Nature Link
Log in

Philosophy of Science for Machine Learning

Core Issues and New Perspectives

  • Book
  • Open Access
  • © 2026

You have full access to this open access Book

Download book PDF
Download book EPUB

Overview

Editors:
  1. Juan M. Durán

    1. Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

  2. Giorgia Pozzi

    1. Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

  • This book is open access, which means that you have free and unlimited access
  • Updates philosophical debates on machine learning by applying the philosophy of science
  • Brings together a group of scholars on the issues of machine learning from an epistemological perspective
  • Is entirely conceived from a philosophical perspective

Part of the book series: Synthese Library (SYLI, volume 527)

Buy print copy

Hardcover Book EUR 53.49
Price includes VAT (Germany)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyboxMaxSingleColumnWidth = 480 ;[].slice.call(buybox.querySelectorAll(".buying-option")).forEach(initCollapsibles) function initCollapsibles(buyingOption, index) { var toggle = buyingOption.querySelector(".buying-option-price") buyingOption.classList.remove("expanded") var form = buyingOption.querySelector(".buying-option-form") var priceInfo = buyingOption.querySelector(".price-info") if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth -1 ;[].slice.call(buybox.querySelectorAll(".buying-option")).forEach(function (option, index) { var toggle = option.querySelector(".buying-option-price") var form = option.querySelector(".buying-option-form") var priceInfo = option.querySelector(".price-info") if (allOptionsInitiallyCollapsed || narrowBuyboxArea && index > 0) { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } else { toggle.click() } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

About this book

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.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, books and news in related subjects.

Table of contents (22 chapters)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  3. Justification

    1. Front Matter

      Pages 53-53
      Download chapter PDF

    2. Beyond Transparency: Computational Reliabilism as an Externalist Epistemology of Algorithms

      • Juan M. Durán
      Pages 55-79Open Access
      Download chapter PDF

    3. Challenges for Computational Reliabilism in AI and Other Computational Methods

      • Ramón Alvarado
      Pages 81-106Open Access
      Download chapter PDF

    4. Can XAI Justify?

      • Carlos Zednik, Philippe Verreault-Julien
      Pages 107-129Open Access
      Download chapter PDF

  5. Scientific Understanding and Interpretability

    1. Front Matter

      Pages 195-195
      Download chapter PDF

    2. Explanation Hacking: The Perils of Algorithmic Recourse

      • Emily Sullivan, Atoosa Kasirzadeh
      Pages 197-220Open Access
      Download chapter PDF

    3. Stakes and Understanding the Decisions of Artificial Intelligent Systems

      • Eva Schmidt
      Pages 221-242Open Access
      Download chapter PDF

  6. Scientific Models and Representation

    1. Front Matter

      Pages 243-243
      Download chapter PDF

    2. Representation Learning Without Representationalism

      • Phillip Hintikka Kieval
      Pages 245-272Open Access
      Download chapter PDF

    3. Artificial Neural Nets and the Representation of Human Concepts

      • Timo Freiesleben
      Pages 273-294Open Access
      Download chapter PDF

    4. Defining Formal Validity Criteria for Machine Learning Models

      • Chiara Manganini, Giuseppe Primiero
      Pages 295-312Open Access
      Download chapter PDF
Back to top

Editors and Affiliations

  • Technology, Policy and Management, Delft University of Technology, Delft, The Netherlands

    Juan M. Durán, Giorgia Pozzi

About the editors

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.

Accessibility Information

PDF accessibility summary

This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at [email protected]. Please note that a more accessible version of this eBook is available as ePub.

EPUB accessibility summary

This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.2 Level AA standards. It features a navigable table of contents, structured headings, and alternative text for images, ensuring smooth, intuitive navigation and comprehension. The text is reflowable and resizable, with sufficient contrast. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at [email protected].

Bibliographic Information

  • Book Title: Philosophy of Science for Machine Learning

  • Book Subtitle: Core Issues and New Perspectives

  • Editors: Juan M. Durán, Giorgia Pozzi

  • Series Title: Synthese Library

  • DOI: https://doi.org/10.1007/978-3-032-03083-2

  • Publisher: Springer Cham

  • eBook Packages: Religion and Philosophy, Philosophy and Religion (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2026

  • Hardcover ISBN: 978-3-032-03082-5Published: 09 December 2025

  • Softcover ISBN: 978-3-032-03085-6Due: 23 December 2026

  • eBook ISBN: 978-3-032-03083-2Published: 08 December 2025

  • Series ISSN: 0166-6991

  • Series E-ISSN: 2542-8292

  • Edition Number: 1

  • Number of Pages: XXIII, 506

  • Topics: Philosophy of Science, Epistemology, Artificial Intelligence, Philosophy of Technology

Keywords

Publish with us

Policies and ethics

Back to top