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Niklas Kühl
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Machine learning operations (mlops): Overview, definition, and architecture
D Kreuzberger, N Kühl, S Hirschl
IEEE Access, 2023
1492023
Machine Learning in Artificial Intelligence: Towards a Common Understanding
N Kühl, M Goutier, R Hirt, G Satzger
Hawaii International Conference on System Sciences (HICSS-52), 2019
772019
Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media
N Kühl, M Mühlthaler, M Goutier
Electronic Markets 30, 351-367, 2020
702020
Virtual sensors
D Martin, N Kühl, G Satzger
Business & Information Systems Engineering 63, 315-323, 2021
622021
Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review.
P Hemmer, M Schemmer, M Vössing, N Kühl
PACIS, 78, 2021
592021
AI-based resource allocation: Reinforcement learning for adaptive auto-scaling in serverless environments
L Schuler, S Jamil, N Kühl
2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet …, 2021
542021
Literature vs. Twitter: Empirical insights on customer needs in e-mobility
N Kühl, M Goutier, A Ensslen, P Jochem
Journal of cleaner production 213, 508-520, 2019
522019
Deal: Deep evidential active learning for image classification
P Hemmer, N Kühl, J Schöffer
Deep Learning Applications, Volume 3, 171-192, 2022
402022
Needmining: Identifying Micro Blog Data Containing Customer Needs
N Kuehl, J Scheurenbrand, G Satzger
Proceedings of the 24th European Conference of Information Systems (ECIS) 24, 2016
36*2016
Do you comply with AI? - Personalized explanations of learning algorithms and their impact on employees' compliance behavior
N Kühl, J Lobana, C Meske
40th International Conference on Information Systems (ICIS), 2019
352019
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
M Schemmer, P Hemmer, N Kühl, C Benz, G Satzger
Thirtieth European Conference on Information Systems (ECIS 2022), 2022
322022
How to conduct rigorous supervised machine learning in information systems research: the supervised machine learning report card
N Kühl, R Hirt, L Baier, B Schmitz, G Satzger
Communications of the Association for Information Systems 48 (1), 46, 2021
302021
How to cope with change?-preserving validity of predictive services over time
L Baier, N Kühl, G Satzger
Hawaii International Conference on System Sciences, 2019
302019
Human vs. supervised machine learning: Who learns patterns faster?
N Kühl, M Goutier, L Baier, C Wolff, D Martin
Cognitive Systems Research, 2022
292022
Cognitive computing for customer profiling: meta classification for gender prediction
R Hirt, N Kühl, G Satzger
Electronic Markets 29 (1), 93-106, 2019
282019
Holistically defining e-Mobility: a modern approach to systematic literature reviews
J Scheurenbrand, C Engel, F Peters, N Kuehl
KIT Scientific Publishing 7692, 17-27, 2015
272015
"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making
J Schoeffer, N Kuehl, Y Machowski
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT) 2022, 2022
262022
Handling Concept Drifts in Regression Problems--the Error Intersection Approach
L Baier, M Hofmann, N Kühl, M Mohr, G Satzger
HICSS-54, 2020
252020
An End-to-End Process Model for Supervised Machine Learning Classification: From Problem to Deployment in Information Systems
R Hirt, N Kühl, G Satzger
TWELFTH INTERNATIONAL CONFERENCE ON DESIGN SCIENCE RESEARCH IN INFORMATION …, 2017
242017
A Meta-Analysis on the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
M Schemmer, P Hemmer, M Nitsche, N Kühl, M Vössing
AAAI /ACM Conference on Artificial Intelligence, Ethics and Society (AIES) 2022, 2022
212022
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