Open AccessArticle
A Novel Metric for Evaluating the Stability of XAI Explanations
Author’s Name: Falko Gawantka* 1
, Franz Just 1, Marina Savelyeva 1, Markus Wappler 2, Jörg Lässig 1, 2
, Franz Just 1, Marina Savelyeva 1, Markus Wappler 2, Jörg Lässig 1, 21 University of Applied Sciences Zittau/Görlitz, Faculty of Electrical Engineering and Computer Science, Görlitz, 02826, Germany
2 Fraunhofer IOSB, Advanced System Technology (AST), Görlitz, 02826, Germany
*whom correspondence should be addressed. E-mail: falko.gawantka@hszg.de
Adv. Sci. Technol. Eng. Syst. J. 9(1), 133–142 (2024);
DOI: 10.25046/aj090113
Received: 15 November 2023, Revised: 16 January 2024, Accepted: 17 January 2024, Published Online: 21 February 2024
(This article belongs to the SP15 (Special Issue on Innovation in Computing, Engineering Science & Technology 2023) & Section Artificial Intelligence in Computer Science (CAI))
Automated systems are increasingly exerting influence on our lives, evident in scenarios like AI-driven candidate screening for jobs or loan applications. These scenarios often rely on eXplainable Artificial Intelligence (XAI) algorithms to meet legal requirements and provide understandable insights into critical processes. However, a significant challenge arises when some XAI methods lack determinism, resulting in the generation of different explanations for identical inputs (i.e., the same data instances and prediction model). The question of explanation stability becomes paramount in such cases. In this study, we introduce two intuitive methods for assessing the stability of XAI algorithms. A taxonomy was developed to categorize the evaluation criteria and the ideas were expanded to create an objective metric to classify the XAI algorithms based on their explanation stability.
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