Machine learning operations (MLOps): Overview, definition, and architecture D Kreuzberger, N Kühl, S Hirschl IEEE Access, 2023 | 291 | 2023 |
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 | 89 | 2019 |
Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review. P Hemmer, M Schemmer, M Vössing, N Kühl PACIS, 78, 2021 | 87 | 2021 |
Virtual sensors D Martin, N Kühl, G Satzger Business & Information Systems Engineering 63, 315-323, 2021 | 87 | 2021 |
Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media N Kühl, M Mühlthaler, M Goutier Electronic Markets 30 (2), 351-367, 2020 | 85 | 2020 |
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 | 76 | 2021 |
Artificial intelligence and machine learning N Kühl, M Schemmer, M Goutier, G Satzger Electronic Markets 32 (4), 2235-2244, 2022 | 61 | 2022 |
Deal: Deep evidential active learning for image classification P Hemmer, N Kühl, J Schöffer Deep Learning Applications, Volume 3, 171-192, 2022 | 56 | 2022 |
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 | 51 | 2019 |
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 | 50 | 2022 |
"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 | 47 | 2022 |
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 | 46 | 2021 |
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations M Schemmer, N Kühl, C Benz, A Bartos, G Satzger 🏆 ACM Conference on Intelligent User Interfaces (ACM IUI), 2023 | 45 | 2023 |
Human vs. supervised machine learning: Who learns patterns faster? N Kühl, M Goutier, L Baier, C Wolff, D Martin Cognitive Systems Research, 2022 | 43 | 2022 |
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 | 42 | 2022 |
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 | 39* | 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 | 37 | 2019 |
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 | 35 | 2019 |
Designing transparency for effective human-AI collaboration M Vössing, N Kühl, M Lind, G Satzger Information Systems Frontiers 24 (3), 877-895, 2022 | 34 | 2022 |
Cognitive computing for customer profiling: meta classification for gender prediction R Hirt, N Kühl, G Satzger Electronic Markets 29 (1), 93-106, 2019 | 34 | 2019 |