Author ORCID Identifier

https://orcid.org/ 0000-0002-2718-5426

Document Type

Article

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Disciplines

Computer Sciences

Publication Details

Proceedings for the 28th AIAI Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, December 7-8, 2020

Abstract

The ultimate goal of Explainable Artificial Intelligence is to build models that possess both high accuracy and degree of explainability. Understanding the inferences of such models can be seen as a process that discloses the relationships between their input and output. These relationships can be represented as a set of inference rules which are usually not explicit within a model. Scholars have proposed several methods for extracting rules from data-driven machine-learned models. However, limited work exists on their comparison. This study proposes a novel comparative approach to evaluate and compare the rulesets produced by four post-hoc rule extractors by employing six quantitative metrics. Findings demonstrate that these metrics can actually help identify superior methods over the others thus are capable of successfully modelling distinctively aspects of explainability.

DOI

https://doi.org/10.21427/z4x3-3f86

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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