Grimsley, Christopher (2020) Causal and Non-Causal Explanations of Artificial Intelligence. In: UNSPECIFIED.

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Abstract

Deep neural networks (DNNs), a particularly effective type of artificial intelligence, currently lack a scientific explanation. The philosophy of science is uniquely equipped to handle this problem. Computer science has attempted, unsuccessfully, to explain DNNs. I review these contributions, then identify shortcomings in their approaches. The complexity of DNNs prohibits the articulation of relevant causal relationships between their parts, and as a result causal explanations fail. I show that many non-causal accounts, though more promising, also fail to explain AI. This highlights a problem with existing accounts of scientific explanation rather than with AI or DNNs.

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Item Type:	Conference or Workshop Item (UNSPECIFIED)
Creators:	Creators Email ORCID Grimsley, Christopher christopher.grimsley@uky.edu
Keywords:	AI, Machine Learning, Neural Network, non-causal, explanation
Subjects:	General Issues > Causation Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning
Depositing User:	Christopher Grimsley
Date Deposited:	23 Jun 2020 04:45
Last Modified:	23 Jun 2020 04:45
Item ID:	17359
Subjects:	General Issues > Causation Specific Sciences > Computer Science Specific Sciences > Artificial Intelligence General Issues > Explanation Specific Sciences > Artificial Intelligence > Machine Learning
Date:	6 March 2020
URI:	https://philsci-archive.pitt.edu/id/eprint/17359

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