Explainable AI and causal understanding : Counterfactual approaches considered

Journal article


Baron, Sam. (2023). Explainable AI and causal understanding : Counterfactual approaches considered. Minds and Machines. 33(2), pp. 347-377. https://doi.org/10.1007/s11023-023-09637-x
AuthorsBaron, Sam
Abstract

The counterfactual approach to explainable AI (XAI) seeks to provide understanding of AI systems through the provision of counterfactual explanations. In a recent systematic review, Chou et al. (Inform Fus 81:59–83, 2022) argue that the counterfactual approach does not clearly provide causal understanding. They diagnose the problem in terms of the underlying framework within which the counterfactual approach has been developed. To date, the counterfactual approach has not been developed in concert with the approach for specifying causes developed by Pearl (Causality: Models, reasoning, and inference. Cambridge University Press, 2000) and Woodward (Making things happen: A theory of causal explanation. Oxford University Press, 2003). In this paper, I build on Chou et al.’s work by applying the Pearl-Woodward approach. I argue that the standard counterfactual approach to XAI is capable of delivering causal understanding, but that there are limitations on its capacity to do so. I suggest a way to overcome these limitations.

Keywordscounterfactuals; explanation; causation; interventions; understanding; XAI
Year2023
JournalMinds and Machines
Journal citation33 (2), pp. 347-377
PublisherSpringer
ISSN0924-6495
Digital Object Identifier (DOI)https://doi.org/10.1007/s11023-023-09637-x
Scopus EID2-s2.0-8516137954
Open accessPublished as ‘gold’ (paid) open access
Page range347-377
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online09 Jun 2023
Publication process dates
Accepted14 May 2023
Deposited20 Dec 2023
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https://acuresearchbank.acu.edu.au/item/900w4/explainable-ai-and-causal-understanding-counterfactual-approaches-considered

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