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ORCIDNiklas Kühl 0001
Niklas Kuehl 0001
Person information
- affiliation: University of Bayreuth, Germany
- affiliation: Karlsruhe Institute of Technology, Germany
Other persons with the same name
- Niklas Kühl 0002
![[0000-0002-4229-1358]](https://dblp.uni-trier.de./img/orcid-mark.12x12.png "0000-0002-4229-1358")
— Hamburg Ship Model Basin, Hamburg, Germany (and 1 more)
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2020 – today
- 2025
[j17]Robin Hirt, Niklas Kühl, Dominik Martin, Gerhard Satzger:
Enabling inter-organizational analytics in business networks through meta machine learning. Inf. Technol. Manag. 26(1): 57-81 (2025)- [j16]Jakob Schöffer, Johannes Jakubik, Michael Vössing, Niklas Kühl, Gerhard Satzger:
AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions. J. Artif. Intell. Res. 82 (2025) - [i78]Philipp Spitzer, Dominik Martin, Laurin Eichberger, Niklas Kühl:
Towards a Problem-Oriented Domain Adaptation Framework for Machine Learning. CoRR abs/2501.04528 (2025) - 2024
- [j15]Jan Bode, Niklas Kühl, Dominik Kreuzberger, Carsten Holtmann:
Toward Avoiding the Data Mess: Industry Insights From Data Mesh Implementations. IEEE Access 12: 95402-95416 (2024) - [j14]Johannes Jakubik, Michael Vössing, Niklas Kühl, Jannis Walk, Gerhard Satzger:
Data-Centric Artificial Intelligence. Bus. Inf. Syst. Eng. 66(4): 507-515 (2024) - [j13]Jannis Walk, Max Schemmer, Niklas Kühl, Gerhard Satzger:
Image-Mining-Based Decision Support Systems: Design Knowledge and its Evaluation in Tool Wear Analysis. Commun. Assoc. Inf. Syst. 54: 44 (2024) - [j12]Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, Adam Perer:
The Impact of Imperfect XAI on Human-AI Decision-Making. Proc. ACM Hum. Comput. Interact. 8(CSCW1): 1-39 (2024) - [c84]Leopold Müller, Patrick Hemmer, Moritz Queisner, Igor Sauer, Simeon Allmendinger, Johannes Jakubik, Michael Vössing, Niklas Kühl:
Redefining the Laparoscopic Spatial Sense: AI-Based Intra- and Postoperative Measurement from Stereoimages. AAAI 2024: 22948-22954 - [c83]Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. CHI 2024: 836:1-836:18 - [c82]Joshua Holstein, Philipp Spitzer, Marieke Hoell, Michael Vössing, Niklas Kühl:
Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics. ECIS 2024 - [c81]Philipp Spitzer, Niklas Kühl, Marc Goutier, Manuel Kaschura, Gerhard Satzger:
Transferring Domain Knowledge with (X)AI-Based Learning Systems. ECIS 2024 - [c80]Domenique Zipperling, Simeon Allmendinger, Lukas Struppek, Niklas Kühl:
Collafuse: Navigating Limited Resources and Privacy in Collaborative Generative AI. ECIS 2024 - [c79]Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
A Critical Survey on Fairness Benefits of Explainable AI. FAccT 2024: 1579-1595 - [c78]Niklas Kühl, Thomas Setzer, Hansjörg Fromm, Michael Vössing:
Introduction to the Minitrack on Service Analytics. HICSS 2024: 1486-1487 - [c77]Philipp Spitzer, Sebastian Celis, Dominik Martin, Niklas Kühl, Gerhard Satzger:
Looking Through the Deep Glasses: How Large Language Models Enhance Explainability of Deep Learning Models. MuC 2024: 566-570 - [c76]Philipp Spitzer, Marc Goutier, Niklas Kühl, Gerhard Satzger:
(X)AI as a Teacher: Learning with Explainable Artificial Intelligence. MuC 2024: 571-576 - [c75]Ivan Iliash, Simeon Allmendinger, Felix Meissen, Niklas Kühl, Daniel Rückert:
Interactive Generation of Laparoscopic Videos with Diffusion Models. DGM4MICCAI@MICCAI 2024: 109-118 - [i77]Philipp Spitzer, Joshua Holstein, Patrick Hemmer, Michael Vössing, Niklas Kühl, Dominik Martin, Gerhard Satzger:
On the Effect of Contextual Information on Human Delegation Behavior in Human-AI collaboration. CoRR abs/2401.04729 (2024) - [i76]Domenique Zipperling, Simeon Allmendinger, Lukas Struppek, Niklas Kühl:
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI. CoRR abs/2402.19105 (2024) - [i75]Luca Deck, Jan-Laurin Müller, Conradin Braun, Domenique Zipperling, Niklas Kühl:
Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairness. CoRR abs/2403.20089 (2024) - [i74]Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, Gerhard Satzger:
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence. CoRR abs/2404.00029 (2024) - [i73]Luca Deck, Astrid Schomäcker, Timo Speith, Jakob Schöffer, Lena Kästner, Niklas Kühl:
Mapping the Potential of Explainable Artificial Intelligence (XAI) for Fairness Along the AI Lifecycle. CoRR abs/2404.18736 (2024) - [i72]Joshua Holstein, Philipp Spitzer, Marieke Hoell, Michael Vössing, Niklas Kühl:
Understanding Data Understanding: A Framework to Navigate the Intricacies of Data Analytics. CoRR abs/2405.07658 (2024) - [i71]Philipp Spitzer, Niklas Kühl, Marc Goutier, Manuel Kaschura, Gerhard Satzger:
Transferring Domain Knowledge with (X)AI-Based Learning Systems. CoRR abs/2406.01329 (2024) - [i70]Ivan Iliash, Simeon Allmendinger, Felix Meissen, Niklas Kühl, Daniel Rückert:
Interactive Generation of Laparoscopic Videos with Diffusion Models. CoRR abs/2406.06537 (2024) - [i69]Niklas Kühl, Christian Meske, Maximilian Nitsche, Jodie Lobana:
Investigating the Role of Explainability and AI Literacy in User Compliance. CoRR abs/2406.12660 (2024) - [i68]Simeon Allmendinger, Domenique Zipperling, Lukas Struppek, Niklas Kühl:
CollaFuse: Collaborative Diffusion Models. CoRR abs/2406.14429 (2024) - [i67]Sven Eckhardt, Niklas Kühl, Mateusz Dolata, Gerhard Schwabe:
A Survey of AI Reliance. CoRR abs/2408.03948 (2024) - [i66]Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling, Niklas Kühl:
A Multivocal Literature Review on Privacy and Fairness in Federated Learning. CoRR abs/2408.08666 (2024) - [i65]Lars Böcking, Leopold Müller, Niklas Kühl:
Utilizing Data Fingerprints for Privacy-Preserving Algorithm Selection in Time Series Classification: Performance and Uncertainty Estimation on Unseen Datasets. CoRR abs/2409.08636 (2024) - [i64]Philipp Spitzer, Joshua Holstein, Katelyn Morrison, Kenneth Holstein, Gerhard Satzger, Niklas Kühl:
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration. CoRR abs/2409.12809 (2024) - 2023
- [j11]Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl:
Machine Learning Operations (MLOps): Overview, Definition, and Architecture. IEEE Access 11: 31866-31879 (2023) - [j10]Philipp Spitzer, Niklas Kühl, Daniel Heinz, Gerhard Satzger:
ML-Based Teaching Systems: A Conceptual Framework. Proc. ACM Hum. Comput. Interact. 7(CSCW2): 1-25 (2023) - [c74]Patrick Hemmer, Lukas Thede, Michael Vössing, Johannes Jakubik, Niklas Kühl:
Learning to Defer with Limited Expert Predictions. AAAI 2023: 6002-6011 - [c73]Philipp Spitzer, Joshua Holstein, Michael Vössing, Niklas Kühl:
On the Perception of Difficulty: Differences between Human and AI. AutomationXP@CHI 2023 - [c72]Franziska Haller, Max Schemmer, Niklas Kühl, Carsten Holtmann:
Conceptualizing a Multi-Sided Platform for Cloud Computing Resource Trading. ECIS 2023 - [c71]Jakob Schoeffer, Johannes Jakubik, Michael Vössing, Niklas Kühl, Gerhard Satzger:
On the Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making. HHAI 2023: 46-59 - [c70]Marco Geiger, Dominik Martin, Niklas Kühl:
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images. HICSS 2023: 825-834 - [c69]Lucas Baier, Tim Schlör, Jakob Schoeffer, Niklas Kühl:
Detecting Concept Drift with Neural Network Model Uncertainty. HICSS 2023: 835-844 - [c68]Hansjörg Fromm, Niklas Kühl, Gerhard Satzger, Thomas Setzer:
Introduction to the Minitrack on Service Analytics. HICSS 2023: 1345 - [c67]Max Schemmer, Andrea Bartos, Philipp Spitzer, Patrick Hemmer, Niklas Kühl, Jonas Liebschner, Gerhard Satzger:
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice. ICIS 2023 - [c66]Johannes Jakubik, Michal Muszynski, Michael Vössing, Niklas Kühl, Thomas Brunschwiler:
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation. IGARSS 2023: 5638-5641 - [c65]Max Schemmer, Niklas Kühl, Carina Benz, Andrea Bartos, Gerhard Satzger:
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations. IUI 2023: 410-422 - [i63]Johannes Jakubik, Michal Muszynski, Michael Vössing, Niklas Kühl, Thomas Brunschwiler:
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation. CoRR abs/2301.09318 (2023) - [i62]Jan Bode, Niklas Kühl, Dominik Kreuzberger, Sebastian Hirschl:
Data Mesh: Motivational Factors, Challenges, and Best Practices. CoRR abs/2302.01713 (2023) - [i61]Max Schemmer, Niklas Kühl, Carina Benz, Andrea Bartos, Gerhard Satzger:
Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations. CoRR abs/2302.02187 (2023) - [i60]Max Schemmer, Joshua Holstein, Niklas Bauer, Niklas Kühl, Gerhard Satzger:
Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series. CoRR abs/2302.03302 (2023) - [i59]Jannis Walk, Niklas Kühl, Michael Saidani, Jürgen Schatte:
Artificial Intelligence for Sustainability: Facilitating Sustainable Smart Product-Service Systems with Computer Vision. CoRR abs/2303.13540 (2023) - [i58]Robin Hirt, Niklas Kühl, Dominik Martin, Gerhard Satzger:
Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning. CoRR abs/2303.15834 (2023) - [i57]Patrick Hemmer, Lukas Thede, Michael Vössing, Johannes Jakubik, Niklas Kühl:
Learning to Defer with Limited Expert Predictions. CoRR abs/2304.07306 (2023) - [i56]Jakob Schoeffer, Johannes Jakubik, Michael Vössing, Niklas Kühl, Gerhard Satzger:
On the Interdependence of Reliance Behavior and Accuracy in AI-Assisted Decision-Making. CoRR abs/2304.08804 (2023) - [i55]Philipp Spitzer, Joshua Holstein, Michael Vössing, Niklas Kühl:
On the Perception of Difficulty: Differences between Humans and AI. CoRR abs/2304.09803 (2023) - [i54]Franziska Haller, Max Schemmer, Niklas Kühl, Carsten Holtmann:
Conceptualizing A Multi-Sided Platform For Cloud Computing Resource Trading. CoRR abs/2305.07399 (2023) - [i53]Philipp Spitzer, Niklas Kühl, Daniel Heinz, Gerhard Satzger:
ML-Based Teaching Systems: A Conceptual Framework. CoRR abs/2305.07681 (2023) - [i52]Katelyn Morrison, Philipp Spitzer, Violet Turri, Michelle Feng, Niklas Kühl, Adam Perer:
The Impact of Imperfect XAI on Human-AI Decision-Making. CoRR abs/2307.13566 (2023) - [i51]Maximilian Nitsche, S. Karthik Mukkavilli, Niklas Kühl, Thomas Brunschwiler:
AB2CD: AI for Building Climate Damage Classification and Detection. CoRR abs/2309.01066 (2023) - [i50]Max Schemmer, Andrea Bartos, Philipp Spitzer, Patrick Hemmer, Niklas Kühl, Jonas Liebschner, Gerhard Satzger:
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice. CoRR abs/2310.02108 (2023) - [i49]Luca Deck, Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
A Critical Survey on Fairness Benefits of XAI. CoRR abs/2310.13007 (2023) - [i48]Leopold Müller, Patrick Hemmer, Moritz Queisner, Igor Sauer, Simeon Allmendinger, Johannes Jakubik, Michael Vössing, Niklas Kühl:
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages. CoRR abs/2311.09744 (2023) - [i47]Simeon Allmendinger, Patrick Hemmer, Moritz Queisner, Igor Sauer, Leopold Müller, Johannes Jakubik, Michael Vössing, Niklas Kühl:
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation. CoRR abs/2312.03043 (2023) - 2022
- [j9]Moritz von Zahn, Stefan Feuerriegel, Niklas Kuehl:
The Cost of Fairness in AI: Evidence from E-Commerce. Bus. Inf. Syst. Eng. 64(3): 335-348 (2022) - [j8]Julius Peter Landwehr, Niklas Kühl, Jannis Walk, Mario Gnädig:
Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems. Bus. Inf. Syst. Eng. 64(6): 707-728 (2022) - [j7]Niklas Kühl, Marc Goutier, Lucas Baier, Clemens Wolff, Dominik Martin:
Human vs. supervised machine learning: Who learns patterns faster? Cogn. Syst. Res. 76: 78-92 (2022) - [j6]Niklas Kühl, Max Schemmer, Marc Goutier, Gerhard Satzger:
Artificial intelligence and machine learning. Electron. Mark. 32(4): 2235-2244 (2022) - [j5]Michael Vössing, Niklas Kühl, Matteo Lind, Gerhard Satzger:
Designing Transparency for Effective Human-AI Collaboration. Inf. Syst. Frontiers 24(3): 877-895 (2022) - [c64]Max Schemmer, Patrick Hemmer, Maximilian Nitsche, Niklas Kühl, Michael Vössing:
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making. AIES 2022: 617-626 - [c63]Patrick Hemmer, Max Schemmer, Lara Riefle, Nico Rosellen, Michael Vössing, Niklas Kühl:
Factors that Influence the Adoption of Human-AI Collaboration in Clinical Decision-Making. ECIS 2022 - [c62]Max Schemmer, Niklas Kühl, Carina Benz, Gerhard Satzger:
On the Influence of Explainable AI on Automation Bias. ECIS 2022 - [c61]Jakob Schoeffer, Niklas Kühl, Yvette Machowski:
"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making. FAccT 2022: 1616-1628 - [c60]Enrico Bunde, Niklas Kühl, Christian Meske:
Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content. HICSS 2022: 1-10 - [c59]Thi Thu Hang Do, Markus Dobler, Niklas Kühl:
What to prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development. HICSS 2022: 1-10 - [c58]Hansjörg Fromm, Gerhard Satzger, Niklas Kühl, Thomas Setzer:
Introduction to the Minitrack on Service Analytics. HICSS 2022: 1 - [c57]Patrick Hemmer, Niklas Kühl, Jakob Schoeffer:
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry. HICSS 2022: 1-10 - [c56]Dominik Martin, Simon Heinzel, Johannes Kunze von Bischhoffshausen, Niklas Kühl:
Deep Learning Strategies for Industrial Surface Defect Detection Systems. HICSS 2022: 1-10 - [c55]Max Schemmer, Niklas Kühl, Gerhard Satzger:
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Workers Through Explainable Artificial Intelligence. HICSS 2022: 1-10 - [c54]Jakob Schoeffer, Yvette Machowski, Niklas Kühl:
Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making. HICSS 2022: 1-8 - [c53]Martin Maritsch, Kevin Koch, Hauke Thomsen, Niklas Kühl, Matthias Pfäffli, Wolfgang Weinmann, Felix Wortmann:
Driver state prediction from vehicle signals: An evaluation of segmentation approaches. ITSC 2022: 1106-1113 - [c52]Johannes Jakubik, Jakob Schöffer, Vincent Hoge, Michael Vössing, Niklas Kühl:
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making. PKDD/ECML Workshops (1) 2022: 353-368 - [i46]Max Schemmer, Patrick Hemmer, Niklas Kühl, Carina Benz, Gerhard Satzger:
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making. CoRR abs/2204.06916 (2022) - [i45]Max Schemmer, Niklas Kühl, Carina Benz, Gerhard Satzger:
On the Influence of Explainable AI on Automation Bias. CoRR abs/2204.08859 (2022) - [i44]Patrick Hemmer, Max Schemmer, Lara Riefle, Nico Rosellen, Michael Vössing, Niklas Kühl:
Factors that influence the adoption of human-AI collaboration in clinical decision-making. CoRR abs/2204.09082 (2022) - [i43]Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
On the Relationship Between Explanations, Fairness Perceptions, and Decisions. CoRR abs/2204.13156 (2022) - [i42]Patrick Hemmer, Max Schemmer, Niklas Kühl, Michael Vössing, Gerhard Satzger:
On the Effect of Information Asymmetry in Human-AI Teams. CoRR abs/2205.01467 (2022) - [i41]Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl:
Machine Learning Operations (MLOps): Overview, Definition, and Architecture. CoRR abs/2205.02302 (2022) - [i40]Max Schemmer, Patrick Hemmer, Maximilian Nitsche, Niklas Kühl, Michael Vössing:
A Meta-Analysis on the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making. CoRR abs/2205.05126 (2022) - [i39]Jakob Schoeffer, Niklas Kühl, Yvette Machowski:
"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Making. CoRR abs/2205.05758 (2022) - [i38]Philipp Spitzer, Niklas Kühl, Marc Goutier:
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer. CoRR abs/2207.00497 (2022) - [i37]Johannes Jakubik, Jakob Schöffer, Vincent Hoge, Michael Vössing, Niklas Kühl:
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making. CoRR abs/2208.04181 (2022) - [i36]Marco Geiger, Dominik Martin, Niklas Kühl:
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images. CoRR abs/2209.11299 (2022) - [i35]Jakob Schoeffer, Maria De-Arteaga, Niklas Kühl:
On Explanations, Fairness, and Appropriate Reliance in Human-AI Decision-Making. CoRR abs/2209.11812 (2022) - [i34]Johannes Jakubik, Michael Vössing, Niklas Kühl, Jannis Walk, Gerhard Satzger:
Data-centric Artificial Intelligence. CoRR abs/2212.11854 (2022) - 2021
- [j4]Dominik Martin, Niklas Kühl, Gerhard Satzger:
Virtual Sensors. Bus. Inf. Syst. Eng. 63(3): 315-323 (2021) - [j3]Niklas Kühl, Robin Hirt, Lucas Baier, Björn Schmitz, Gerhard Satzger:
How to Conduct Rigorous Supervised Machine Learning in Information Systems Research: The Supervised Machine Learning Report Card. Commun. Assoc. Inf. Syst. 48: 46 (2021) - [c51]Lucia Schuler, Somaya Jamil, Niklas Kühl:
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments. CCGRID 2021: 804-811 - [c50]Jakob Schöffer, Niklas Kühl:
Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems. CSCW Companion 2021: 153-157 - [c49]Jakob Schöffer, Niklas Kühl, Isabel Valera:
A Ranking Approach to Fair Classification. COMPASS 2021: 115-125 - [c48]Max Schemmer, Daniel Heinz, Lucas Baier, Michael Vössing, Niklas Kühl:
Conceptualizing Digital Resilience for AI-based Information Systems. ECIS 2021 - [c47]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. ECIS 2021 - [c46]Lucas Baier, Vincent Kellner, Niklas Kühl, Gerhard Satzger:
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets. HICSS 2021: 1-10 - [c45]Tobias Enders, Niklas Kühl, Jannis Walk, Marc Muff:
Designing Chemical Emergency Response Systems Based on Open Data. HICSS 2021: 1-10 - [c44]Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl:
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. HICSS 2021: 1-10 - [c43]Niklas Kühl, Dominik Martin, Clemens Wolff, Melanie Volkamer:
"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics. HICSS 2021: 1-10 - [c42]Thomas Setzer, Hansjörg Fromm, Niklas Kühl, Gerhard Satzger:
Introduction to the Minitrack on Service Analytics. HICSS 2021: 1 - [c41]Lucas Baier, Niklas Kühl, Jörg Schmitt:
Increasing Robustness for Machine Learning Services in Challenging Environments: Limited Resources and No Label Feedback. IntelliSys (1) 2021: 837-856 - [c40]Jakob Schöffer, Yvette Machowski, Niklas Kühl:
A Study on Fairness and Trust Perceptions in Automated Decision Making. IUI Workshops 2021 - [c39]Patrick Hemmer, Max Schemmer, Michael Vössing, Niklas Kühl:
Human-AI Complementarity in Hybrid Intelligence Systems: A Structured Literature Review. PACIS 2021: 78 - [c38]Dominik Martin, Niklas Kühl, Marcel Schwenk:
Towards a Reference Architecture for Future Industrial Internet of Things Networks. CBI (2) 2021: 1-9 - [i33]Niklas Kühl, Gerhard Satzger:
Needmining: Designing Digital Support to Elicit Needs from Social Media. CoRR abs/2101.06146 (2021) - [i32]Jakob Schöffer, Niklas Kühl, Isabel Valera:
A Ranking Approach to Fair Classification. CoRR abs/2102.04565 (2021) - [i31]Jakob Schöffer, Yvette Machowski, Niklas Kühl:
A Study on Fairness and Trust Perceptions in Automated Decision Making. CoRR abs/2103.04757 (2021) - [i30]Christoph Sager, Patrick Zschech, Niklas Kühl:
labelCloud: A Lightweight Domain-Independent Labeling Tool for 3D Object Detection in Point Clouds. CoRR abs/2103.04970 (2021) - [i29]Patrick Zschech, Jannis Walk, Kai Heinrich, Michael Vössing, Niklas Kühl:
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems. CoRR abs/2104.11600 (2021) - [i28]Lucas Baier, Tim Schlör, Jakob Schöffer, Niklas Kühl:
Detecting Concept Drift With Neural Network Model Uncertainty. CoRR abs/2107.01873 (2021) - [i27]Jakob Schöffer, Niklas Kühl:
Appropriate Fairness Perceptions? On the Effectiveness of Explanations in Enabling People to Assess the Fairness of Automated Decision Systems. CoRR abs/2108.06500 (2021) - [i26]Dominik Martin, Niklas Kühl, Marcel Schwenk:
Towards a Reference Architecture for Future Industrial Internet of Things Networks. CoRR abs/2109.00833 (2021) - [i25]Jakob Schöffer, Yvette Machowski, Niklas Kühl:
Perceptions of Fairness and Trustworthiness Based on Explanations in Human vs. Automated Decision-Making. CoRR abs/2109.05792 (2021) - [i24]Dominik Martin, Simon Heinzel, Johannes Kunze von Bischhoffshausen, Niklas Kühl:
Deep Learning Strategies for Industrial Surface Defect Detection Systems. CoRR abs/2109.11304 (2021) - [i23]Enrico Bunde, Niklas Kühl, Christian Meske:
Fake or Credible? Towards Designing Services to Support Users' Credibility Assessment of News Content. CoRR abs/2109.13336 (2021) - [i22]Thi Thu Hang Do, Markus Dobler, Niklas Kühl:
What to Prioritize? Natural Language Processing for the Development of a Modern Bug Tracking Solution in Hardware Development. CoRR abs/2109.13825 (2021) - [i21]Max Schemmer, Niklas Kühl, Gerhard Satzger:
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence. CoRR abs/2109.13827 (2021) - [i20]Patrick Hemmer, Niklas Kühl, Jakob Schöffer:
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry. CoRR abs/2110.09023 (2021) - 2020
- [j2]Niklas Kühl, Marius Mühlthaler, Marc Goutier:
Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media. Electron. Mark. 30(2): 351-367 (2020) - [c37]Clemens Wolff, Niklas Kühl, Gerhard Satzger:
Engineering Industrial Service Systems: Design and Evaluation of System-Oriented Service Delivery. DESRIST 2020: 407-419 - [c36]Dominik Martin, Niklas Kühl, Johannes Kunze von Bischhoffshausen, Gerhard Satzger:
System-Wide Learning in Cyber-Physical Service Systems: A Research Agenda. DESRIST 2020: 457-468 - [c35]Tristan Karb, Niklas Kühl, Robin Hirt, Varvara Glivici-Cotruta:
A Network-based Transfer Learning Approach to Improve Sales Forecasting of New Products. ECIS 2020 - [c34]Niklas Kühl, Hansjörg Fromm, Gerhard Satzger, Thomas Setzer:
Introduction to the Minitrack on Service Analytics. HICSS 2020: 1-2 - [c33]Niklas Kühl, Cameron G. Walker, Melanie Reuter-Oppermann:
Introduction to the Minitrack on Optimization of and the Use of IT for Healthcare Processes. HICSS 2020: 1 - [c32]Dominik Martin, Philipp Spitzer, Niklas Kühl:
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs. HICSS 2020: 1-10 - [c31]Jannis Walk, Niklas Kühl, Jonathan Schäfer:
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry. HICSS 2020: 1-10 - [c30]Clemens Wolff, Niklas Kühl, Gerhard Satzger:
Enabling System-Oriented Service Delivery in Industrial Maintenance: A Meta-method for Predicting Industrial Costs of Downtime. IESS 2020: 92-106 - [c29]Jannis Walk, Robin Hirt, Niklas Kühl, Erik R. Hersløv:
Half-Empty or Half-Full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime. IESS 2020: 107-120 - [c28]Lucas Baier, Jakob Schöffer, Niklas Kühl:
Utilizing Adaptive AI-based Information Systems to Analyze the Effectiveness of Policy Measures in the Fight of COVID-19. ICIS 2020 - [c27]Patrick Hemmer, Niklas Kühl, Jakob Schöffer:
DEAL: Deep Evidential Active Learning for Image Classification. ICMLA 2020: 865-870 - [c26]Alexander Treiss, Jannis Walk, Niklas Kühl:
An Uncertainty-Based Human-in-the-Loop System for Industrial Tool Wear Analysis. ECML/PKDD (5) 2020: 85-100 - [c25]Robin Hirt, Niklas Kühl, Yusuf Peker, Gerhard Satzger:
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting. CBI (1) 2020: 20-29 - [c24]Lucas Baier, Josua Reimold, Niklas Kühl:
Handling Concept Drift for Predictions in Business Process Mining. CBI (1) 2020: 76-83 - [c23]Lucas Baier, Niklas Kühl, Gerhard Satzger, Marcel Hofmann, Marisa Mohr:
Handling Concept Drifts in Regression Problems - the Error Intersection Approach. Wirtschaftsinformatik (Zentrale Tracks) 2020: 210-224 - [p7]Maria Maleshkova, Niklas Kühl, Philipp Jussen:
Introduction to Smart Service Management. Smart Service Management 2020: 3-5 - [p6]Dominik Martin, Niklas Kühl, Maria Maleshkova:
Grasping the Terminology: Smart Services, Smart Service Systems, and Cyber-Physical Systems. Smart Service Management 2020: 7-21 - [p5]Michael Vössing, Niklas Kühl:
Industrial Maintenance in the Digital World. Smart Service Management 2020: 23-32 - [p4]Niklas Kühl, Hansjörg Fromm, Jakob Schöffer, Gerhard Satzger:
Service Analytics: Putting the "Smart" in Smart Services. Smart Service Management 2020: 151-159 - [p3]Maria Maleshkova, Niklas Kühl, Philipp Jussen:
Introduction to Smart Service Use Cases. Smart Service Management 2020: 163-165 - [p2]Lena Eckstein, Niklas Kühl, Gerhard Satzger:
Designing a Smart Service for Customer Need Identification in B2B Ticketing Systems. Smart Service Management 2020: 167-178 - [p1]Dominik Martin, Niklas Kühl, Johannes Kunze von Bischhoffshausen:
Smart Services: A Condition Monitoring Use Case Utilizing System-Wide Analyses. Smart Service Management 2020: 179-191 - [e3]Maria Maleshkova, Niklas Kühl, Philipp Jussen:
Smart Service Management - Design Guidelines and Best Practices. Springer 2020, ISBN 978-3-030-58181-7 [contents] - [e2]Maria Henriqueta Nóvoa, Monica Dragoicea, Niklas Kühl:
Exploring Service Science - 10th International Conference, IESS 2020, Porto, Portugal, February 5-7, 2020, Proceedings. Lecture Notes in Business Information Processing 377, Springer 2020, ISBN 978-3-030-38723-5 [contents] - [i19]Niklas Kühl, Jodie Lobana, Christian Meske:
Do you comply with AI? - Personalized explanations of learning algorithms and their impact on employees' compliance behavior. CoRR abs/2002.08777 (2020) - [i18]Niklas Kühl, Jan Scheurenbrand, Gerhard Satzger:
Needmining: Identifying micro blog data containing customer needs. CoRR abs/2003.05917 (2020) - [i17]Robin Hirt, Akash Srivastava, Carlos Berg, Niklas Kühl:
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability. CoRR abs/2003.13070 (2020) - [i16]Jannis Walk, Robin Hirt, Niklas Kühl, Erik R. Hersløv:
Half-empty or half-full? A Hybrid Approach to Predict Recycling Behavior of Consumers to Increase Reverse Vending Machine Uptime. CoRR abs/2003.13304 (2020) - [i15]Lucas Baier, Marcel Hofmann, Niklas Kühl, Marisa Mohr, Gerhard Satzger:
Handling Concept Drifts in Regression Problems - the Error Intersection Approach. CoRR abs/2004.00438 (2020) - [i14]Svenja Laing, Niklas Kühl:
Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings. CoRR abs/2004.03323 (2020) - [i13]Niklas Kühl, Marc Goutier, Robin Hirt, Gerhard Satzger:
Machine Learning in Artificial Intelligence: Towards a Common Understanding. CoRR abs/2004.04686 (2020) - [i12]Dominik Martin, Philipp Spitzer, Niklas Kühl:
A New Metric for Lumpy and Intermittent Demand Forecasts: Stock-keeping-oriented Prediction Error Costs. CoRR abs/2004.10537 (2020) - [i11]Lucas Baier, Josua Reimold, Niklas Kühl:
Handling Concept Drift for Predictions in Business Process Mining. CoRR abs/2005.05810 (2020) - [i10]Tristan Karb, Niklas Kühl, Robin Hirt, Varvara Glivici-Cotruta:
A network-based transfer learning approach to improve sales forecasting of new products. CoRR abs/2005.06978 (2020) - [i9]Robin Hirt, Niklas Kühl, Yusuf Peker, Gerhard Satzger:
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting. CoRR abs/2005.10698 (2020) - [i8]Lucia Schuler, Somaya Jamil, Niklas Kühl:
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments. CoRR abs/2005.14410 (2020) - [i7]Alexander Treiss, Jannis Walk, Niklas Kühl:
An Uncertainty-based Human-in-the-loop System for Industrial Tool Wear Analysis. CoRR abs/2007.07129 (2020) - [i6]Patrick Hemmer, Niklas Kühl, Jakob Schöffer:
DEAL: Deep Evidential Active Learning for Image Classification. CoRR abs/2007.11344 (2020) - [i5]Jannis Walk, Niklas Kühl, Jonathan Schäfer:
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry. CoRR abs/2008.01053 (2020) - [i4]Niklas Kühl, Dominik Martin, Clemens Wolff, Melanie Volkamer:
"Healthy surveillance": Designing a concept for privacy-preserving mask recognition AI in the age of pandemics. CoRR abs/2010.12026 (2020) - [i3]Lucas Baier, Vincent Kellner, Niklas Kühl, Gerhard Satzger:
Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Sets. CoRR abs/2011.02738 (2020) - [i2]Niklas Kühl, Marc Goutier, Lucas Baier, Clemens Wolff, Dominik Martin:
Human vs. supervised machine learning: Who learns patterns faster? CoRR abs/2012.03661 (2020) - [i1]Lucas Baier, Niklas Kühl, Jakob Schöffer, Gerhard Satzger:
Utilizing Concept Drift for Measuring the Effectiveness of Policy Interventions: The Case of the COVID-19 Pandemic. CoRR abs/2012.03728 (2020)
2010 – 2019
- 2019
- [j1]Robin Hirt, Niklas Kühl, Gerhard Satzger:
Cognitive computing for customer profiling: meta classification for gender prediction. Electron. Mark. 29(1): 93-106 (2019) - [c22]Michael Vössing, Felix Potthoff, Niklas Kühl, Gerhard Satzger:
Designing Useful Transparency to Improve Process Performance - Evidence from an Automated production Line. ECIS 2019 - [c21]Lucas Baier, Niklas Kühl, Gerhard Satzger:
How to Cope with Change? - Preserving Validity of Predictive Services over Time. HICSS 2019: 1-10 - [c20]Niklas Kühl, Marc Goutier, Robin Hirt, Gerhard Satzger:
Machine Learning in Artificial Intelligence: Towards a Common Understanding. HICSS 2019: 1-10 - [c19]Dominik Martin, Niklas Kühl:
Holistic System-Analytics as an Alternative to Isolated Sensor Technology: A Condition Monitoring Use Case. HICSS 2019: 1-8 - [c18]Clemens Wolff, Melanie Reuter-Oppermann, Niklas Kühl:
On the Impact of the Customer Base on the Added Value through System-Oriented Service Delivery in Industrial Maintenance. HICSS 2019: 1-10 - [c17]Clemens Wolff, Steven Kimbrough, Niklas Kühl:
Analyzing the Impact of Strategic Behavior in System-Oriented Service Delivery. ICIS 2019 - [c16]Dominik Martin, Robin Hirt, Niklas Kühl:
Service Systems, Smart Service Systems and Cyber- Physical Systems - What's the difference? Towards a Unified Terminology. Wirtschaftsinformatik 2019: 17-31 - [c15]Niklas Kühl, Dominik Martin, Gerhard Satzger:
Automatically Extracting and Analyzing Customer Needs from Twitter: A "Needmining" Prototype. Wirtschaftsinformatik 2019: 1967-1970 - 2018
- [c14]Robin Hirt, Niklas Kühl, Björn Schmitz, Gerhard Satzger:
Service-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda. HICSS 2018: 1-8 - [c13]Niklas Kühl, Marius Mühlthaler, Marc Goutier:
Automatically Quantifying Customer Need Tweets: Towards a Supervised Machine Learning Approach. HICSS 2018: 1-10 - [c12]Robin Hirt, Niklas Kühl:
Cognition in the Era of Smart Service Systems: Inter-organizational Analytics through Meta and Transfer Learning. ICIS 2018 - [c11]Svenja Laing, Niklas Kühl:
Comfort-as-a-Service: Designing a User-Oriented Thermal Comfort Artifact for Office Buildings. ICIS 2018 - [c10]Clemens Wolff, Michael Vössing, Niklas Kühl:
Using Monte-Carlo simulation to Measure the Business-Relevant Impact of Planning uncertainty on field Service Delivery. WSC 2018: 3205-3216 - [e1]Gerhard Satzger, Lia Patrício, Mohamed Zaki, Niklas Kühl, Peter Hottum:
Exploring Service Science - 9th International Conference, IESS 2018, Karlsruhe, Germany, September 19-21, 2018, Proceedings. Lecture Notes in Business Information Processing 331, Springer 2018, ISBN 978-3-030-00712-6 [contents] - 2017
- [b1]Niklas Kühl:
Needmining: automated analytical support for customer need elicitation. Karlsruhe Institute of Technology, Germany, 2017, pp. 1-200 - [c9]Robin Hirt, Niklas Kühl:
Abbildung kognitiver Fähigkeiten mit Metamodellen. GI-Jahrestagung 2017: 2301-2307 - [c8]Fabian Hunke, Ronny Schüritz, Niklas Kuehl:
Towards a Unified Approach to Identify Business Model Patterns: A Case of E-Mobility Services. IESS 2017: 182-196 - 2016
- [c7]Niklas Kühl, Jan Scheurenbrand, Gerhard Satzger:
Needmining: identifying micro Blog Data containing Customer Needs. ECIS 2016: Research Paper 185 - [c6]Niklas Kühl, Jonas Lehner:
Programming for refugees - an active learning approach for teaching Java to heterogeneous groups. GI-Jahrestagung 2016: 1175-1178 - [c5]Niklas Kühl, Marc Goutier:
"Need tweets": new insights about customer needs from micro blog data in the field of E-mobility. GI-Jahrestagung 2016: 1363-1376 - [c4]Niklas Kuehl:
Needmining: Towards Analytical Support for Service Design. IESS 2016: 187-200 - [c3]Niklas Kühl, Jan Scheurenbrand, Gerhard Satzger:
"Needs from Tweets": Towards Deriving Customer Needs From Micro Blog Data (Extended Abstract). MKWI 2016: 1229-1232 - [c2]Niklas Kühl, Marc Goutier:
Needmining: Evaluating a Whitelist-Based Assignment Method to Quantify Customer Needs from Micro Blog Data. OR 2016: 165-170 - [c1]Lena Eckstein, Niklas Kühl, Gerhard Satzger:
Towards Extracting Customer Needs from Incident Tickets in IT Services. CBI (1) 2016: 200-207
Coauthor Index
[j17] [j16] [j14] [j13] [c81] [c77] [c76] [i77] [i74] [i71] [i64] [j10] [c71] [c68] [c67] [c65] [i61] [i60] [i58] [i56] [i53] [i50] [j6] [j5] [c62] [c58] [c55] [i46] [i45] [i42] [i34] [j4] [j3] [c46] [c42] [i33] [i21] [c37] [c36] [c34] [c30] [c25] [c23] [p4] [p2] [i18] [i15] [i13] [i9] [i3] [i1] [j1] [c22] [c21] [c20] [c15] [c14] [e1] [c7] [c3] [c1]
Jakob Schöffer
aka: Jakob Schoeffer
aka: Jakob Schoeffer
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