Dr. Stefanie Schwaar from the Fraunhofer Institute for Industrial Mathematics ITWM won the BMBF’s call for tenders for funding among young female AI researchers. She will establish and head her own research group at the mathematical institute from August 2020. Under the title “EP-KI: Decision Support for Business Management Processes with the Help of New AI Methods” they will work at the interface of business management and Artificial Intelligence (AI). An excellent network of administration, business and research supports the group, which is funded by the Federal Ministry of Education and Research.
Today many decisions in companies and administrations are based on manually evaluated data sets. At the same time, public administrations in particular are facing a major technological upheaval in the coming years, which will lead to the digitalization of numerous progress. The Corona pandemic demonstrated that digitalization must make an even faster progress. Confirmed by the AI strategy of the government, there is great potential in the application of comprehensible AI methods to business management issues.
Stefanie Schwaar will use the BMBF funding to establish a research group explicitly for young female scientists in the field of artificial intelligence starting in summer 2020. The aim of this research funding is to establish a leading role of Germany in AI research as well as to increase the proportion of young female scientists in AI research. New staff will be recruited for this research group – with preference given to women with the same qualifications. The development of AI methods – especially machine learning algorithms – is divided into three research areas within the project: Prediction, Structural Breaks and Auto-Encoders.
A potential use case is the detection of anomalous requests, e.g. requests with typing errors or even fraud. To detect potentially anomalous requests, various existing Machine Learning (ML) methods can be used. However, these are usually not interpretable. The researchers develop a procedure that detects the anomalous requests and suggests a corrected version to the user.
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