A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence

TítuloA Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence
AutoresIlia Stepin, Jose M. Alonso, Alejandro Catala, Martin Pereira-Fariña
TipoArtículo de revista
Fonte IEEE Access, IEEE, 2021.
RankProvisionally ranked Q1 in Computer Science (all) by SJR 2019
ISSN2169-3536
DOI10.1109/ACCESS.2021.3051315
AbstractA number of algorithms in the field of artificial intelligence offer poorly interpretable decisions. To disclose the reasoning behind such algorithms, their output can be explained by means of so-called evidence-based (or factual) explanations. Alternatively, contrastive and counterfactual explanations justify why the output of the algorithms is not any different and how it could be changed, respectively. It is of crucial importance to bridge the gap between theoretical approaches to contrastive and counterfactual explanation and the corresponding computational frameworks. In this work we conduct a systematic literature review which provides readers with a thorough and reproducible analysis of the interdisciplinary research field under study. We first examine theoretical foundations of contrastive and counterfactual accounts of explanation. Then, we report the state-of-the-art computational frameworks for contrastive and counterfactual explanation generation. In addition, we analyze how grounded such frameworks are on the insights from the inspected theoretical approaches. As a result, we highlight a variety of properties of the approaches under study and reveal a number of shortcomings thereof. Moreover, we define a taxonomy regarding both theoretical and practical approaches to contrastive and counterfactual explanation.
Palabras chaveComputational Intelligence, Contrastive Explanations, Counterfactuals, Explainable Artificial Intelligence, Systematic Literature Review