A fuzzy linguistic supported framework to increase Artificial Intelligence intelligibility for subject matter experts
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Bernabé-Moreno, J., & Wildberger, K. (2019). A fuzzy linguistic supported framework to increase Artificial Intelligence intelligibility for subject matter experts. Procedia Computer Science, 162, 865-872.
The application of artificial intelligence (AI) techniques in the decision making processes is more widespread in the industry than ever before. Yet, one of the most critical show-stoppers is the communication gap between the machine learning (ML) models and the experts community. On one hand, the output of ML is often not intelligible for experts, in spite of the latest advances in explainable AI. On the other hand, the expert knowledge, rarely completely present in the available data, but rather in the heads of the experts, needs to be connected to the data-driven insights created by the ML model. In this paper we first identify the most critical situations with a manifest intelligibility gap and then propose a framework supported by fuzzy linguistic modelling techniques to close this gap. In addition, we present its integration into the end-to-end decision making flow, from data gathering to the execution and evaluation and we show the output of our approach with practical examples.