Conference article in proceedings
Explainable and Transparent AI and Multi-Agent Systems - 3rd International Workshop, EXTRAAMAS 2021, Revised Selected Papers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 12688 LNAI
AbstractMany techniques have been proposed in recent years that attempt to explain results of image classifiers, notably for the case when the classifier is a deep neural network. This paper presents an implementation of the Contextual Importance and Utility method for explaining image classifications. It is an R package that can be used with the most usual image classification models. The paper shows results for typical benchmark images, as well as for a medical data set of gastro-enterological images. For comparison, results produced by the LIME method are included. Results show that CIU produces similar or better results than LIME with significantly shorter calculation times. However, the main purpose of this paper is to bring the existence of this package to general knowledge and use, rather than comparing with other explanation methods.
Publisher Copyright: © 2021, Springer Nature Switzerland AG.
Contextual importance and utility, Deep neural network, Explainable artificial intelligence, Image classification
Främling , K , Knapic̆ , S & Malhi , A 2021 , ciu.image : An R Package for Explaining Image Classification with Contextual Importance and Utility . in D Calvaresi , A Najjar , M Winikoff & K Främling (eds) , Explainable and Transparent AI and Multi-Agent Systems - 3rd International Workshop, EXTRAAMAS 2021, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 12688 LNAI , Springer , pp. 55-62 , International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems , Virtual, Online , 03/05/2021 . https://doi.org/10.1007/978-3-030-82017-6_4