- Deep Learning-Aided Research and the Aim-of-Science Controversy.Yukinori Onishi - forthcoming - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie:1-19.details
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Scientific Inference with Interpretable Machine Learning: Analyzing Models to Learn About Real-World Phenomena.Timo Freiesleben, Gunnar König, Christoph Molnar & Álvaro Tejero-Cantero - 2024 - Minds and Machines 34 (3):1-39.details
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Machine learning in healthcare and the methodological priority of epistemology over ethics.Thomas Grote - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.details
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The Explanatory Role of Machine Learning in Molecular Biology.Fridolin Gross - forthcoming - Erkenntnis:1-21.details
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Listening to algorithms: The case of self‐knowledge.Casey Doyle - forthcoming - European Journal of Philosophy.details
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Do ML models represent their targets?Emily Sullivan - forthcoming - Philosophy of Science.details
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Explainability, Public Reason, and Medical Artificial Intelligence.Michael Da Silva - 2023 - Ethical Theory and Moral Practice 26 (5):743-762.details
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Explainable AI and Causal Understanding: Counterfactual Approaches Considered.Sam Baron - 2023 - Minds and Machines 33 (2):347-377.details
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Deep Learning Applied to Scientific Discovery: A Hot Interface with Philosophy of Science.Louis Vervoort, Henry Shevlin, Alexey A. Melnikov & Alexander Alodjants - 2023 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 54 (2):339-351.details
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Models, Algorithms, and the Subjects of Transparency.Hajo Greif - 2022 - In Vincent C. Müller (ed.), Philosophy and Theory of Artificial Intelligence 2021. Berlin: Springer. pp. 27-37.details
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Understanding, Idealization, and Explainable AI.Will Fleisher - 2022 - Episteme 19 (4):534-560.details
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The Importance of Understanding Deep Learning.Tim Räz & Claus Beisbart - 2024 - Erkenntnis 89 (5).details
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Karl Jaspers and artificial neural nets: on the relation of explaining and understanding artificial intelligence in medicine.Christopher Poppe & Georg Starke - 2022 - Ethics and Information Technology 24 (3):1-10.details
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Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - 2022 - Philosophy of Science 89 (5):1065-1074.details
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Agree to disagree: the symmetry of burden of proof in human–AI collaboration.Karin Rolanda Jongsma & Martin Sand - 2022 - Journal of Medical Ethics 48 (4):230-231.details
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Scientific Exploration and Explainable Artificial Intelligence.Carlos Zednik & Hannes Boelsen - 2022 - Minds and Machines 32 (1):219-239.details
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Values and inductive risk in machine learning modelling: the case of binary classification models.Koray Karaca - 2021 - European Journal for Philosophy of Science 11 (4):1-27.details
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Hypothesis-driven science in large-scale studies: the case of GWAS.Sumana Sharma & James Read - 2021 - Biology and Philosophy 36 (5):1-21.details
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Two Dimensions of Opacity and the Deep Learning Predicament.Florian J. Boge - 2021 - Minds and Machines 32 (1):43-75.details
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Understanding climate phenomena with data-driven models.Benedikt Knüsel & Christoph Baumberger - 2020 - Studies in History and Philosophy of Science Part A 84 (C):46-56.details
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Why Attention is Not Explanation: Surgical Intervention and Causal Reasoning about Neural Models.Christopher Grimsley, Elijah Mayfield & Julia Bursten - 2020 - Proceedings of the 12th Conference on Language Resources and Evaluation.details
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(1 other version)Computers Are Syntax All the Way Down: Reply to Bozşahin.William J. Rapaport - 2019 - Minds and Machines 29 (2):227-237.details
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Negotiating becoming: a Nietzschean critique of large language models.Simon W. S. Fischer & Bas de Boer - 2024 - Ethics and Information Technology 26 (3):1-12.details
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Reliability in Machine Learning.Thomas Grote, Konstantin Genin & Emily Sullivan - 2024 - Philosophy Compass 19 (5):e12974.details
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Health Digital Twins, Legal Liability, and Medical Practice.Andreas Kuersten - 2023 - American Journal of Bioethics 23 (9):66-69.details
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Epistemic Value of Digital Simulacra for Patients.Eleanor Gilmore-Szott - 2023 - American Journal of Bioethics 23 (9):63-66.details
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Mapping representational mechanisms with deep neural networks.Phillip Hintikka Kieval - 2022 - Synthese 200 (3):1-25.details
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Sources of Understanding in Supervised Machine Learning Models.Paulo Pirozelli - 2022 - Philosophy and Technology 35 (2):1-19.details
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Conceptual challenges for interpretable machine learning.David S. Watson - 2022 - Synthese 200 (2):1-33.details
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Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).details
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Humanistic interpretation and machine learning.Juho Pääkkönen & Petri Ylikoski - 2021 - Synthese 199:1461–1497.details
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On the Opacity of Deep Neural Networks.Anders Søgaard - forthcoming - Canadian Journal of Philosophy:1-16.details
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AI and bureaucratic discretion.Kate Vredenburgh - forthcoming - Inquiry: An Interdisciplinary Journal of Philosophy.details
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On the Philosophy of Unsupervised Learning.David S. Watson - 2023 - Philosophy and Technology 36 (2):1-26.details
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The proper role of history in evolutionary explanations.Thomas A. C. Reydon - 2023 - Noûs 57 (1):162-187.details
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Putting explainable AI in context: institutional explanations for medical AI.Jacob Browning & Mark Theunissen - 2022 - Ethics and Information Technology 24 (2).details
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Understanding climate change with statistical downscaling and machine learning.Julie Jebeile, Vincent Lam & Tim Räz - 2020 - Synthese (1-2):1-21.details
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ML interpretability: Simple isn't easy.Tim Räz - 2024 - Studies in History and Philosophy of Science Part A 103 (C):159-167.details
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Predicting and explaining with machine learning models: Social science as a touchstone.Oliver Buchholz & Thomas Grote - 2023 - Studies in History and Philosophy of Science Part A 102 (C):60-69.details
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Philosophy of science at sea: Clarifying the interpretability of machine learning.Claus Beisbart & Tim Räz - 2022 - Philosophy Compass 17 (6):e12830.details
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Epistemo-ethical constraints on AI-human decision making for diagnostic purposes.Dina Babushkina & Athanasios Votsis - 2022 - Ethics and Information Technology 24 (2).details
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Minds and Machines Special Issue: Machine Learning: Prediction Without Explanation?F. J. Boge, P. Grünke & R. Hillerbrand - 2022 - Minds and Machines 32 (1):1-9.details
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Can Robots Do Epidemiology? Machine Learning, Causal Inference, and Predicting the Outcomes of Public Health Interventions.Alex Broadbent & Thomas Grote - 2022 - Philosophy and Technology 35 (1):1-22.details
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Can Machine Learning Provide Understanding? How Cosmologists Use Machine Learning to Understand Observations of the Universe.Helen Meskhidze - 2023 - Erkenntnis 88 (5):1895-1909.details
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Do artificial intelligence systems understand?Carlos Blanco Pérez & Eduardo Garrido-Merchán - 2024 - Claridades. Revista de Filosofía 16 (1):171-205.details
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Searching for Features with Artificial Neural Networks in Science: The Problem of Non-Uniqueness.Siyu Yao & Amit Hagar - 2024 - International Studies in the Philosophy of Science 37 (1):51-67.details
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On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning.Justin B. Biddle - 2022 - Canadian Journal of Philosophy 52 (3):321-341.details
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A Puzzle concerning Compositionality in Machines.Ryan M. Nefdt - 2020 - Minds and Machines 30 (1):47-75.details
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The predictive reframing of machine learning applications: good predictions and bad measurements.Alexander Martin Mussgnug - 2022 - European Journal for Philosophy of Science 12 (3):1-21.details
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Analogue Models and Universal Machines. Paradigms of Epistemic Transparency in Artificial Intelligence.Hajo Greif - 2022 - Minds and Machines 32 (1):111-133.details
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