PhilSci Archive

Causal and Non-Causal Explanations of Artificial Intelligence

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

[img]
Preview
Text
ExplanationAttention.pdf

Download (149kB) | Preview

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.


Export/Citation: EndNote | BibTeX | Dublin Core | ASCII/Text Citation (Chicago) | HTML Citation | OpenURL
Social Networking:
Share |

Item Type: Conference or Workshop Item (UNSPECIFIED)
Creators:
CreatorsEmailORCID
Grimsley, Christopherchristopher.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

Monthly Views for the past 3 years

Monthly Downloads for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item