1st Workshop on Conceptual Modeling Meets Artificial Intelligence and Data-Driven Decision Making (CMAI 2020) (Co-Organization, Schütz)
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Artificial intelligence (AI) is front and center in the data-driven revolution that has been taking place in the last couple of years with the increasing availability of large amounts of data (?big data?) in virtually every domain. The now dominant paradigm of data-driven AI, powered by sophisticated machine learning algorithms, employs big data to build intelligent applications and support fact-based decision making. The focus of data-driven AI is on learning (domain) models and keeping those models up-to-date by using statistical methods over big data, in contrast to the manual modeling approach prevalent in traditional, knowledge-based AI.
While data-driven AI has led to significant breakthroughs, it also comes with a number of disadvantages. First, models generated by machine learning algorithms often cannot be inspected and understood by a human being, thus lacking explainability. Furthermore, integration of preexisting domain knowledge into learned models ? prior to or after learning ? is difficult. Finally, correct application of data-driven AI depends on the domain, problem, and organizational context while considering human aspects as well. Conceptual modeling can be the key to applying data-driven AI in a meaningful, correct, and time-efficient way while improving maintainability, usability, and explainability.