Moving towards explainable artificial intelligence using fuzzy rule-based networks in decision-making process

Advanced Machine Learning and Artificial Intelligence techniques are very pow-erful in predictive tasks and they are getting more popular as decision making tools across many industries and fields. However, they are mostly weak in ex-plaining the inference and internal process and they are referred to as black-box models. Fuzzy Rule Based Network is a powerful white-box technique which maps well the external inputs, intermediate latent variables and outputs a modular approach based on Fuzzy Logic and it is capable of dealing with complexity and linguistic uncertainty in decision making process. To improve the performance of Fuzzy Rule Based Network, it requires to be tuned and optimized to increase its accuracy, transparency and efficiency. In this paper, a method is proposed to tune the Fuzzy Rule Based Network by using Fuzzy C-Mean and Genetic Algorithm for rule reduction and tuning membership functions and also Backward Selection techniques for pruning and input and branch selection. A case study in transport and telecommuting is used to illustrate the performance of the proposed method. The results show the Fuzzy Rule Based Network’s ability to explain the internal process of decision making and its capabilities in transparency, interpretability and in moving towards Explainable Artificial Intelligence (XAI).