89 citations · 253 across the 20 of their papers we have counts for
20 papers
Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition
Gaole He, Patrick Hemmer, Michael Vössing +2
In recent years, the rapid development of AI systems has brought about the benefits of intelligent services but also concerns about security and reliability. By fostering appropria…
Improving Label Error Detection and Elimination with Uncertainty Quantification
Johannes Jakubik, Michael Vössing, Manil Maskey +2
Identifying and handling label errors can significantly enhance the accuracy of supervised machine learning models. Recent approaches for identifying label errors demonstrate that…
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Patrick Hemmer, Max Schemmer, Niklas Kühl +2
Artificial intelligence (AI) has the potential to significantly enhance human performance across various domains. Ideally, collaboration between humans and AI should result in comp…
Human Delegation Behavior in Human-AI Collaboration: The Effect of Contextual Information
Philipp Spitzer, Joshua Holstein, Patrick Hemmer +4
The integration of artificial intelligence (AI) into human decision-making processes at the workplace presents both opportunities and challenges. One promising approach to leverage…
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
Simeon Allmendinger, Patrick Hemmer, Moritz Queisner +5
Recent advances in synthetic imaging open up opportunities for obtaining additional data in the field of surgical imaging. This data can provide reliable supplements supporting sur…
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Leopold Müller, Patrick Hemmer, Moritz Queisner +5
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths. While this tas…
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Johannes Jakubik, Daniel Weber, Patrick Hemmer +2
Information systems increasingly leverage artificial intelligence (AI) and machine learning (ML) to generate value from vast amounts of data. However, ML models are imperfect and c…
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
Benedikt Blumenstiel, Johannes Jakubik, Hilde Kühne +1
While semantic segmentation has seen tremendous improvements in the past, there are still significant labeling efforts necessary and the problem of limited generalization to classe…
Learning to Defer with Limited Expert Predictions
Patrick Hemmer, Lukas Thede, Michael Vössing +2
Recent research suggests that combining AI models with a human expert can exceed the performance of either alone. The combination of their capabilities is often realized by learnin…
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
Patrick Hemmer, Monika Westphal, Max Schemmer +3
Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by conside…
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation
Johannes Jakubik, Michal Muszynski, Michael Vössing +2
Climate change results in an increased probability of extreme weather events that put societies and businesses at risk on a global scale. Therefore, near real-time mapping of natur…
Data-Centric Artificial Intelligence
Johannes Jakubik, Michael Vössing, Niklas Kühl +2
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effe…
On the Influence of Cognitive Styles on Users' Understanding of Explanations
Lara Riefle, Patrick Hemmer, Carina Benz +2
Artificial intelligence (AI) is becoming increasingly complex, making it difficult for users to understand how the AI has derived its prediction. Using explainable AI (XAI)-methods…
An Empirical Evaluation of Predicted Outcomes as Explanations in Human-AI Decision-Making
Johannes Jakubik, Jakob Schöffer, Vincent Hoge +2
In this work, we empirically examine human-AI decision-making in the presence of explanations based on predicted outcomes. This type of explanation provides a human decision-maker…
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Johannes Jakubik, Benedikt Blumenstiel, Michael Vössing +1
Business analytics and machine learning have become essential success factors for various industries - with the downside of cost-intensive gathering and labeling of data. Few-shot…
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
Patrick Hemmer, Sebastian Schellhammer, Michael Vössing +2
Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to d…
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
Max Schemmer, Patrick Hemmer, Maximilian Nitsche +2
Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and…
On the Effect of Information Asymmetry in Human-AI Teams
Patrick Hemmer, Max Schemmer, Niklas Kühl +2
Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas. Teaming between AI and humans may even l…
Factors that influence the adoption of human-AI collaboration in clinical decision-making
Patrick Hemmer, Max Schemmer, Lara Riefle +3
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner. Especially in clinical decision-ma…
Online Emotions During the Storming of the U.S. Capitol: Evidence from the Social Media Network Parler
Johannes Jakubik, Michael Vössing, Dominik Bär +2
The storming of the U.S. Capitol on January 6, 2021 has led to the killing of 5 people and is widely regarded as an attack on democracy. The storming was largely coordinated throug…