PSIdeepqualicision AI efficiently learns how to set decision-making and optimization algorithm (EOA) parameters so that nearly any EOA method that works on business process data can auto-adjust itself. The core of PSIdeepqualicision AI is a machine learning method based on the automatic detection of KPI conflicting goals in business process data using extended fuzzy logic. Self-learning, automated Qualitative Labeling, i.e. the evaluation of whether the available data has led to desirable or undesirable KPI results in the process, can be performed by the user comprehensibly in the sense of Explainable AI (XAI) thanks to PSIdeepqualicision AI. The goal conflict analysis helps to organize the process data in such a way that the Deep-Qualicision-AI-algorithm can independently recognize in which situations how to label. Data directly labeled by human analysts (data scientists) is not needed anymore. In this way, measures for AI-supported business process optimization can be initiated by the user with interpretable KPI labels or controlled automatically by the analysis of qualitative optimizations in a way that adds value as well as conserves resources and is sustainable.
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