Philosophy of Science 87 (4):568-589 (2020)
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| Abstract |
Scientists depend on complex computational systems that are often ineliminably opaque, to the detriment of our ability to give scientific explanations and detect artifacts. Some philosophers have s...
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| Keywords | Machine Learning Computer Simulation Transparency Computation Explanation |
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| DOI | 10.1086/709729 |
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References found in this work BETA
Thinking About Mechanisms.Peter Machamer, Lindley Darden & Carl F. Craver - 2000 - Philosophy of Science 67 (1):1-25.
How the Machine ‘Thinks’: Understanding Opacity in Machine Learning Algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1):205395171562251.
Rethinking Mechanistic Explanation.Stuart Glennan - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S342-353.
View all 12 references / Add more references
Citations of this work BETA
The Right to Explanation.Kate Vredenburgh - 2022 - Wiley: Journal of Political Philosophy 30 (2):209-229.
What is Interpretability?Adrian Erasmus, Tyler D. P. Brunet & Eyal Fisher - 2021 - Philosophy and Technology 34:833–862.
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.Kathleen Creel & Deborah Hellman - 2022 - Canadian Journal of Philosophy 52 (1):26-43.
Algorithmic Bias: Senses, Sources, Solutions.Sina Fazelpour & David Danks - 2021 - Philosophy Compass 16 (8):e12760.
Algorithmic and Human Decision Making: For a Double Standard of Transparency.Mario Günther & Atoosa Kasirzadeh - 2022 - AI and Society 37 (1):375-381.
View all 23 citations / Add more citations
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