Search Results for author:
Found 16 papers, 8 papers with code
Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence
Our work provides researchers with a theoretical foundation of complementarity in human-AI decision-making and demonstrates that leveraging sources of complementarity potential constitutes a viable pathway toward effective human-AI collaboration.
On the Effect of Contextual Information on Human Delegation Behavior in Human-AI collaboration
In this work, we explore the effects of providing contextual information on human decisions to delegate instances to an AI.
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
We demonstrate the usage of state-of-the-art text-to-image architectures in the context of laparoscopic imaging with regard to the surgical removal of the gallbladder as an example.
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths.
Towards Effective Human-AI Decision-Making: The Role of Human Learning in Appropriate Reliance on AI Advice
We hypothesize that, in addition to the mental model, human learning is a key mediator of appropriate reliance and, thus, CTP.
Improving the Efficiency of Human-in-the-Loop Systems: Adding Artificial to Human Experts
Hence, human-in-the-loop (HITL) extensions to ML models add a human review for instances that are difficult to classify.
Learning to Defer with Limited Expert Predictions
In this paper, we propose a three-step approach to reduce the number of expert predictions required to train learning to defer algorithms.
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
In an experimental study with 196 participants, we show that task performance and task satisfaction improve through AI delegation, regardless of whether humans are aware of the delegation.
Instance Selection Mechanisms for Human-in-the-Loop Systems in Few-Shot Learning
Few-shot learning addresses this challenge and reduces data gathering and labeling costs by learning novel classes with very few labeled data.
Forming Effective Human-AI Teams: Building Machine Learning Models that Complement the Capabilities of Multiple Experts
In this work, we propose an approach that trains a classification model to complement the capabilities of multiple human experts.
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making
However, we find no effect of explanations on users' performance compared to sole AI predictions.
On the Effect of Information Asymmetry in Human-AI Teams
Over the last years, the rising capabilities of artificial intelligence (AI) have improved human decision-making in many application areas.
Factors that influence the adoption of human-AI collaboration in clinical decision-making
Recent developments in Artificial Intelligence (AI) have fueled the emergence of human-AI collaboration, a setting where AI is a coequal partner.
Should I Follow AI-based Advice? Measuring Appropriate Reliance in Human-AI Decision-Making
However, recent work has shown that AI advice is not always beneficial, as humans have shown to be unable to ignore incorrect AI advice, essentially representing an over-reliance on AI.
Utilizing Active Machine Learning for Quality Assurance: A Case Study of Virtual Car Renderings in the Automotive Industry
Computer-generated imagery of car models has become an indispensable part of car manufacturers' advertisement concepts.
DEAL: Deep Evidential Active Learning for Image Classification
By replacing the softmax standard output of a CNN with the parameters of a Dirichlet density, the model learns to identify data instances that contribute efficiently to improving model performance during training.
