Search Results for author:
Found 18 papers, 8 papers with code
Improving Label Error Detection and Elimination with Uncertainty Quantification
Therefore, we develop a range of novel, model-agnostic algorithms for Uncertainty Quantification-Based Label Error Detection (UQ-LED), which combine the techniques of confident learning (CL), Monte Carlo Dropout (MCD), model uncertainty measures (e. g., entropy), and ensemble learning to enhance label error detection.
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.
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.
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
To address this problem, zero-shot semantic segmentation makes use of large self-supervised vision-language models, allowing zero-shot transfer to unseen classes.
On the Perception of Difficulty: Differences between Humans and AI
With the increased adoption of artificial intelligence (AI) in industry and society, effective human-AI interaction systems are becoming increasingly important.
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.
Toward Foundation Models for Earth Monitoring: Generalizable Deep Learning Models for Natural Hazard Segmentation
However, DL-based approaches are designed for one specific task in a single geographic region based on specific frequency bands of satellite data.
Data-Centric Artificial Intelligence
Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems.
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.
A Picture is Worth a Collaboration: Accumulating Design Knowledge for Computer-Vision-based Hybrid Intelligence Systems
For this purpose, we consider the design of such systems from a hybrid intelligence (HI) perspective and aim to derive prescriptive design knowledge for CV-based HI systems.
