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Link Uncertainty, Implementation, and ML Opacity: A Reply to Tamir and Shech

Sullivan, Emily (2022) Link Uncertainty, Implementation, and ML Opacity: A Reply to Tamir and Shech. Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech. pp. 341-345.

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Abstract

This chapter responds to Michael Tamir and Elay Shech’s chapter “Understanding from Deep Learning Models in Context.”


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Item Type: Published Article or Volume
Creators:
CreatorsEmailORCID
Sullivan, Emilyeesullivan29@gmail.com0000-0002-2073-5384
Keywords: Machine learning opacity; explanation; understanding; representation
Subjects: General Issues > Data
Specific Sciences > Computer Science
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
Depositing User: Dr. Emily Sullivan
Date Deposited: 04 Dec 2022 23:55
Last Modified: 04 Dec 2022 23:55
Item ID: 21511
Journal or Publication Title: Scientific Understanding and Representation (Eds) Insa Lawler, Kareem Khalifa & Elay Shech
Publisher: Routledge
Official URL: https://www.routledge.com/Scientific-Understanding...
Subjects: General Issues > Data
Specific Sciences > Computer Science
General Issues > Explanation
Specific Sciences > Artificial Intelligence > Machine Learning
General Issues > Models and Idealization
Date: 2022
Page Range: pp. 341-345
URI: https://philsci-archive-dev.library.pitt.edu/id/eprint/21511

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