| Machine learning operations (MLOps): Overview, definition, and architecture D Kreuzberger, N Kühl, S Hirschl IEEE Access, 2023 | 409 | 2023 |
| Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review. P Hemmer, M Schemmer, M Vössing, N Kühl PACIS, 78, 2021 | 102 | 2021 |
| 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 | 100 | 2019 |
| Virtual sensors D Martin, N Kühl, G Satzger Business & Information Systems Engineering 63, 315-323, 2021 | 99 | 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 | 98 | 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 | 91 | 2021 |
| Artificial intelligence and machine learning N Kühl, M Schemmer, M Goutier, G Satzger Electronic Markets 32 (4), 2235-2244, 2022 | 86 | 2022 |
| 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 | 63 | 2023 |
| 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 | 63 | 2022 |
| Deal: Deep evidential active learning for image classification P Hemmer, N Kühl, J Schöffer Deep Learning Applications, Volume 3, 171-192, 2022 | 59 | 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 | 55 | 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 | 55 | 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 | 53 | 2019 |
| Human vs. supervised machine learning: Who learns patterns faster? N Kühl, M Goutier, L Baier, C Wolff, D Martin Cognitive Systems Research, 2022 | 50 | 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 | 50 | 2021 |
| Designing transparency for effective human-AI collaboration M Vössing, N Kühl, M Lind, G Satzger Information Systems Frontiers 24 (3), 877-895, 2022 | 49 | 2022 |
| Data-Centric Artificial Intelligence J Jakubik, M Vössing, N Kühl, J Walk, G Satzger Business & Information Systems Engineering, 2024 | 44 | 2024 |
| 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 | 42* | 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 | 38 | 2019 |
| On the Influence of Explainable AI on Automation Bias M Schemmer, N Kühl, C Benz, G Satzger CHI Conference on Human Factors in Computing Systems (CHI ’22), Workshop on …, 2022 | 37 | 2022 |
Gerhard SatzgerKarlsruhe Service Research Institute / Institute of Information Systems and Marketing, KITVerified email at kit.edu
