Explainable Artificial Intelligence - Anomaly Detection - Machine Learning
I am part of the DMIR research group since I recieved my Master's Degree in Computer Science from the University of Würzburg in early 2019. As part of the BMBF founded research program DeepScan, I am investigating the explainable detection of anomalous and fraudulent behavior in ERP (Enterprise Resource Planning) systems.
Kobs, K., Zehe, A., Bernstetter, A., Chibane, J., Pfister, J., Tritscher, J., Hotho, A. (2020) “Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels”, ACM Transactions on Social Computing.
Tritscher, J., Ring, M., Schlör, D., Hettinger, L., Hotho, A. (2020) “Evaluation of post-hoc XAI approaches through synthetic tabular data”, International Symposium on Methodologies for Intelligent Systems.