Context-aware Data Plausibility Check Using Machine Learning
Abstract: In the last two decades, computing and storage technologies have experienced enormous advances. Leveraging these recent advances, AI is making the leap from traditional classification use cases to automation of complex systems through advanced machine learning and reasoning algorithms. While the literature on AI algorithms and applications of these algorithms in automation is mature, there is a lack of research on trustworthy AI, i.e. how different industries can trust the developed AI modules. AI algorithms are data-driven, i.e. they learn based on the received data, and also act based on the received status data. Then, an initial step in addressing trustworthy AI is investigating plausibility of the data that is fed to the system. In this work, we study the state-of-the-art data plausibility check approaches. Then, we propose a novel approach that leverages machine learning for an automated data plausibility check. This novel approach is context-aware, i.e. it leverages potential contextual data related to the dataset under investigation for a plausibility check. Performance evaluation results confirm the outstanding performance of the proposed approach in data plausibility check.
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