- A Contextual Approach to Scientific Understanding.Henk W. de Regt & Dennis Dieks - 2005 - Synthese 144 (1):137-170.details
|
|
Extending Ourselves: Computational Science, Empiricism, and Scientific Method.Paul Humphreys - 2004 - New York, US: Oxford University Press.details
|
|
Introduction” to his.D. Lewis - 1986 - Philosophical Papers 2.details
|
|
Understanding, explanation, and unification.Victor Gijsbers - 2013 - Studies in History and Philosophy of Science Part A 44 (3):516-522.details
|
|
(1 other version)Responses to Critics.Jonathan Kvanvig - 2009 - In Adrian Haddock, Alan Millar & Duncan Pritchard (eds.), Epistemic value. New York: Oxford University Press. pp. 339-353.details
|
|
Varieties of Justification in Machine Learning.David Corfield - 2010 - Minds and Machines 20 (2):291-301.details
|
|
Simulation and the sense of understanding.Jaakko Kuorikoski - 2011 - In Paul Humphreys & Cyrille Imbert (eds.), Models, Simulations, and Representations. New York: Routledge. pp. 168-187.details
|
|
The philosophical novelty of computer simulation methods.Paul Humphreys - 2009 - Synthese 169 (3):615 - 626.details
|
|
Scientific explanation and the sense of understanding.J. D. Trout - 2002 - Philosophy of Science 69 (2):212-233.details
|
|
Explanatory unification.Philip Kitcher - 1981 - Philosophy of Science 48 (4):507-531.details
|
|
(1 other version)Studies in the logic of explanation.Carl Gustav Hempel & Paul Oppenheim - 1948 - Philosophy of Science 15 (2):135-175.details
|
|
Explanation and scientific understanding.Michael Friedman - 1974 - Journal of Philosophy 71 (1):5-19.details
|
|
Understanding and the facts.Catherine Elgin - 2007 - Philosophical Studies 132 (1):33 - 42.details
|
|
Dermatologist-level classification of skin cancer with deep neural networks.Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun - 2017 - Nature 542 (7639):115-118.details
|
|
Understanding Deep Learning with Statistical Relevance.Tim Räz - 2022 - Philosophy of Science 89 (1):20-41.details
|
|
The Right to Explanation.Kate Vredenburgh - 2021 - Journal of Political Philosophy 30 (2):209-229.details
|
|
Two Dimensions of Opacity and the Deep Learning Predicament.Florian J. Boge - 2021 - Minds and Machines 32 (1):43-75.details
|
|
Opacity thought through: on the intransparency of computer simulations.Claus Beisbart - 2021 - Synthese 199 (3-4):11643-11666.details
|
|
Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?Chang Ho Yoon, Robert Torrance & Naomi Scheinerman - 2022 - Journal of Medical Ethics 48 (9):581-585.details
|
|
What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research.Markus Langer, Daniel Oster, Timo Speith, Lena Kästner, Kevin Baum, Holger Hermanns, Eva Schmidt & Andreas Sesing - 2021 - Artificial Intelligence 296 (C):103473.details
|
|
Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.details
|
|
Understanding climate change with statistical downscaling and machine learning.Julie Jebeile, Vincent Lam & Tim Räz - 2020 - Synthese (1-2):1-21.details
|
|
What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2021 - Philosophy and Technology 34:833–862.details
|
|
Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics.Florian J. Boge & Paul Grünke - forthcoming - In Andreas Kaminski, Michael Resch & Petra Gehring (eds.), The Science and Art of Simulation II.details
|
|
(2 other versions)The explanation game: a formal framework for interpretable machine learning.David S. Watson & Luciano Floridi - 2020 - Synthese 198 (10):1–32.details
|
|
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.details
|
|
The Pragmatic Turn in Explainable Artificial Intelligence.Andrés Páez - 2019 - Minds and Machines 29 (3):441-459.details
|
|
Scientific understanding and felicitous legitimate falsehoods.Insa Lawler - 2021 - Synthese 198 (7):6859-6887.details
|
|
Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2019 - Philosophy and Technology 34 (2):265-288.details
|
|
Deep learning: A philosophical introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10):e12625.details
|
|
Against Interpretability: a Critical Examination of the Interpretability Problem in Machine Learning.Maya Krishnan - 2020 - Philosophy and Technology 33 (3):487-502.details
|
|
Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.details
|
|
Judging machines: philosophical aspects of deep learning.Arno Schubbach - 2019 - Synthese 198 (2):1807-1827.details
|
|
Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism.Juan M. Durán & Nico Formanek - 2018 - Minds and Machines 28 (4):645-666.details
|
|
What is Understanding? An Overview of Recent Debates in Epistemology and Philosophy of Science.Christoph Baumberger, Claus Beisbart & Georg Brun - 2017 - In Stephen Grimm Christoph Baumberger & Sabine Ammon (eds.), Explaining Understanding: New Perspectives from Epistemology and Philosophy of Science. Routledge. pp. 1-34.details
|
|
Philosophical Papers.Graeme Forbes & David Lewis - 1985 - Philosophical Review 94 (1):108.details
|
|
Understanding and its Relation to Knowledge.Christoph Baumberger - 2011 - In Christoph Jäger Winfrid Löffler (ed.), Epistemology: Contexts, Values, Disagreement. Papers of the 34th International Wittgenstein Symposium. Austrian Ludwig Wittgenstein Society. pp. 16-18.details
|
|
Is understanding a species of knowledge?Stephen R. Grimm - 2006 - British Journal for the Philosophy of Science 57 (3):515-535.details
|
|
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.details
|
|
Peeking inside the black-box: A survey on explainable artificial intelligence (XAI).A. Adadi & M. Berrada - 2018 - IEEE Access 6.details
|
|