Building Cognitive Cities with Explainable Artificial Intelligent Systems

In the era of the Internet of Things and Big Data, data scientists are required to extract valuable knowledge from the given data. This challenging task is not straightforward. Data scientists first analyze, cure and pre-process data. Then, they apply Artificial Intelligence (AI) techniques to automatically extract knowledge from data. However, nowadays the focus is set on knowledge representation and how to enhance the human-machine interaction. Non-expert users, i.e., users without a strong background on AI, require a new generation of explainable AI systems. They are expected to naturally interact with humans, thus providing comprehensible explanations of decisions automatically made. In this paper, we sketch how certain computational intelligence techniques, namely interpretable fuzzy systems, are ready to play a key role in the development of explainable AI systems. Interpretable fuzzy systems have already successfully contributed to build explainable AI systems for cognitive cities.