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Explainable AI and causal understanding : Counterfactual approaches considered
Baron, Sam
Baron, Sam
Author
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.
Keywords
counterfactuals, explanation, causation, interventions, understanding, XAI
Date
2023
Type
Journal article
Journal
Minds and Machines
Book
Volume
33
Issue
2
Page Range
347-377
Article Number
ACU Department
Dianoia Institute of Philosophy
Faculty of Theology and Philosophy
Faculty of Theology and Philosophy
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Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open