Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications

• URL: http://arxiv.org/abs/2103.04244v1
• Date: Sun, 7 Mar 2021 03:11:39 GMT
• Title: Counterfactuals and Causability in Explainable Artificial Intelligence: Theory, Algorithms, and Applications
• Authors: Yu-Liang Chou and Catarina Moreira and Peter Bruza and Chun Ouyang and Joaquim Jorge
• Abstract summary: Some researchers argued that for a machine to achieve a certain degree of human-level explainability, it needs to provide causally understandable explanations. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence.
• Score: 0.20999222360659603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There has been a growing interest in model-agnostic methods that can make deep learning models more transparent and explainable to a user. Some researchers recently argued that for a machine to achieve a certain degree of human-level explainability, this machine needs to provide human causally understandable explanations, also known as causability. A specific class of algorithms that have the potential to provide causability are counterfactuals. This paper presents an in-depth systematic review of the diverse existing body of literature on counterfactuals and causability for explainable artificial intelligence. We performed an LDA topic modelling analysis under a PRISMA framework to find the most relevant literature articles. This analysis resulted in a novel taxonomy that considers the grounding theories of the surveyed algorithms, together with their underlying properties and applications in real-world data. This research suggests that current model-agnostic counterfactual algorithms for explainable AI are not grounded on a causal theoretical formalism and, consequently, cannot promote causability to a human decision-maker. Our findings suggest that the explanations derived from major algorithms in the literature provide spurious correlations rather than cause/effects relationships, leading to sub-optimal, erroneous or even biased explanations. This paper also advances the literature with new directions and challenges on promoting causability in model-agnostic approaches for explainable artificial intelligence.

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