{"id":86,"date":"2021-03-29T09:41:02","date_gmt":"2021-03-29T09:41:02","guid":{"rendered":"http:\/\/cyprusconferences.org\/eais2022\/?page_id=86"},"modified":"2021-07-04T18:34:16","modified_gmt":"2021-07-04T18:34:16","slug":"special-issues","status":"publish","type":"page","link":"https:\/\/cyprusconferences.org\/eais2022\/special-issues\/","title":{"rendered":"Special Issues"},"content":{"rendered":"

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Special Issue<\/h1>\n

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Explainable and Evolving AI Systems (EEAIS)<\/b><\/h2>\n

A Special Issue of<\/span><\/p>\n

Evolving Systems<\/span><\/a> – An Interdisciplinary Journal for Advanced Science and Technology <\/span>Science and Technology<\/span><\/p>\n

Guest Editors<\/b><\/p>\n

Giovanna Castellano, Edwin Lughofer, George A. Papadopoulos.
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Overview<\/b><\/p>\n

Recent rapid advances in Artificial Intelligence (AI) are tied largely to evolving systems that provide remarkable benefits in terms of accurate predictions and reliable decision making. However, beyond high accuracy, it is essential for new AI systems to be understandable by users, who should be able to explain the behavior of AI systems and effectively manage them. Hence, engineers and scientists developing AI systems are focusing more and more their attention on learning explainable models from data that will enable them to gain confidence with the decisions of AI systems and, as a consequence, trust them. More generally, the importance of learning explainable models from data is of help to (i) improve the interaction between users and AI systems in order to tackle complex problems, (ii) easily integrate artificial and human knowledge, and (iii) allow users to validate an AI system with respect to criteria of performance, ethics, safety, causality, etc., thus leading to the ultimate possibility of trusting AI systems for mission-critical applications. Evolving and adaptive models play a fundamental role in the development of AI systems which have the distinction of leveraging explainable knowledge that emphasizes transparency, interpretability and scalability. Evolving models comprise an array of online modeling approaches capable of extracting knowledge from online data streams generated by nonstationary processes. They embody online learning methods and incremental algorithms that evolve or gradually change the extracted knowledge to guarantee life-long learning and self-organization of the granular structure of the model.\u00a0<\/span><\/p>\n

Topics<\/b><\/p>\n

The special issue is intended to focus on the above aspects and will solicit papers that cover original research, overviews and applications of evolving methods in the realm of explainable AI systems.<\/span><\/p>\n

Areas of interest include, but are not limited to:<\/span><\/p>\n