Smart cities aim at improving the quality of life of citizens by accounting for environmental sustainability. However, the increasing complexity and interrelationships of smart city services pose several issues on smart city resilience that are among the challenges of current research. Artificial Intelligence (AI) techniques, such as machine learning, deep learning, computational creativity, and semantics, are indeed used to improve the methods and tools devoted to collecting and analyzing citizens\u2019 opinions, sensing people\u2019s behaviors, organizing knowledge, predicting and forecasting consequences of hazardous events, and, more in general, supporting decisional processes. However, other than enabling and facilitating these activities, AI is also transforming the way people live. The goal of this Special Issue is to stimulate the debate on both AI applications and impacts for smart cities, including the expected benefits and the possible risks originating from their use. The intent is to provide a deep account for novel and disruptive AI approaches to smart city innovation and resilience of urban areas and communities. Focus on the resilience of AI systems themselves, such as designing dependable systems, is also appreciated. Furthermore, the Special Issue welcomes visionary papers related to AI safety, such as handling automation bias and sustaining human performance, and social aspects like handover and human\u2013machine communication.
\nKeywords: AI applications of smart sensor data; AI-driven open innovation for smart cities; AI for crisis management; AI for decision making; AI for environmental sustainability; AI for resilient communities; AI for risk assessment; AI for smart city safety; AI for urban resilience; AI opportunities for smart city innovation; AI safety issues in smart city applications; co-creation approaches of smart city knowledge; computational creativity approaches for rethinking urban areas; critical infrastructure resilience; explainable artificial intelligence for smart city resilience applications; machine learning and\/or deep learning approaches for smart city innovation; semantic technologies for smart cities; smart city ontologies; social media analysis for smart city resilience.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"template":"","format":"standard","topic":[192,430,441,576,597,598,453,528,676,300,696,545,480,483,493,596,606,737,497,500,710],"country":[55],"nsj_language":[57],"class_list":["post-1421","call_for_papers","type-call_for_papers","status-publish","format-standard","hentry","topic-artificial-intelligence","topic-big-data","topic-creative-design","topic-data-management","topic-data-sensing-and-analysis","topic-decision-support-systems","topic-environmental-sustainability","topic-human-computer-interface","topic-infrastructures","topic-innovation","topic-innovative-applications","topic-machine-learning","topic-process-innovation","topic-product-innovation","topic-resilience","topic-risk-management","topic-safety-and-security","topic-sensors","topic-service-innovation","topic-smart-cities","topic-social-media","country-switzerland","nsj_language-english"],"acf":[],"yoast_head":"\n