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Title: Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study
Authors: García-Peñalvo, F. J.
Vázquez-Ingelmo, A.
García-Holgado, A.
Keywords: SLR
systematic literature review
artificial intelligence
machine learning
algorithm recommendation
Issue Date: 6-Feb-2023
Publisher: Tech Science Press
Citation: García-Peñalvo, F. J., Vázquez-Ingelmo, A., & García-Holgado, A. (2023). Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches—A Systematic Literature Review and Mapping Study. Computer Modeling in Engineering & Sciences, 136(2), 1023-1051.
Abstract: The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models’ outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.
ISSN: 1526-1492
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