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Comparing Explanations from Glass-Box and Black-Box Machine-Learning Models
Michał Kuk, Szymon Bobek and Grzegorz J. Nalepa recently presented a paper about explainable AI on the prestigious International Conference on Computational Science 2022 (ICCS 2022). Below you can find a link to the paper.
The paper includes a comparison between explanations generated from black-box algorithms and explanations provided by less accurate glass-box algorithms. The authors aim to highligt the importance of an explanation, which may be more important than the results themselves. May it be that glass-box algorithms would prove to be more useful than gighly accurate black-box models only because their results are easier to explain? To find out, read the text under this link.
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