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Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

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Computer Modeling in Engineering & Sciences 2023年第8期136卷 1023-1051页

作者： francisco joségarcía-penlvo Andrea Vázquez-Ingelmo Alicia garcía-Holgado GRIAL Research Group Computer Science DepartmentUniversity of SalamancaSalamanca37008Spain

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be *** AI is a powerfulmeans 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 *** these reasons,it is important to rely on expert knowledge when applying these ***,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI *** 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 *** systematic review follows the methodology proposed by Kitchenham and other authors in the field of software *** a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were *** reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

关键词： SLR systematic literature review artificial intelligence machine learning algorithm recommendation heuristics explainability

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