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:	Creators Email ORCID Sullivan, Emily eesullivan29@gmail.com 0000-0002-2073-5384
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.pitt.edu/id/eprint/21511

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