Kühl, Niklas

Machine Learning Operations (MLOps): Overview, Definition, and Architecture

Kreuzberger, D., Kühl, N., & Hirschl, S. (2023) · IEEE Access

Software Engineering Techniques and PracticesSoftware System Performance and Reliability

Cited by 766

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Schemmer, M., Hemmer, P., Nitsche, M., Kühl, N., & Vössing, M. (2022) · Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 91

How to Cope with Change? - Preserving Validity of Predictive Services over Time

Baier, L., Kühl, N., & Satzger, G. (2019) · Proceedings of the Annual Hawaii International Conference on System Sciences

Data Stream Mining TechniquesBig Data and Business Intelligence

Cited by 33

Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information

Spitzer, P., Holstein, J., Hemmer, P., Vössing, M., Kühl, N., Martin, D., & Satzger, G. (2025) · Proceedings of the ACM on Human-Computer Interaction

Human-Automation Interaction and SafetySocial Robot Interaction and HRI

Cited by 18

Don’t Be Fooled: The Misinformation Effect of Explanations in Human–AI Collaboration

Spitzer, P., Holstein, J., Morrison, K., Holstein, K., Satzger, G., & Kühl, N. (2026) · International Journal of Human–Computer Interaction

Explainable Artificial Intelligence (XAI)Human-Automation Interaction and Safety

Cited by 12

Learning to Defer with Limited Expert Predictions

Hemmer, P., Thede, L., Vössing, M., Jakubik, J., & Kühl, N. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 10

On the Effect of Information Asymmetry in Human-AI Teams

Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2022) · arXiv

Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)

Cited by 9

Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation

Jakubik, J., Muszynski, M., Vössing, M., Kühl, N., & Brunschwiler, T. (2023) · IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

Remote-Sensing Image ClassificationLandslides and related hazards

Cited by 6