7 papers
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek +2
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffu…
Integrating Causal Machine Learning into Clinical Decision Support Systems: Insights from Literature and Practice
Domenique Zipperling, Lukas Schmidt, Benedikt Hahn +2
Current clinical decision support systems (CDSSs) typically base their predictions on correlation, not causation. In recent years, causal machine learning (ML) has emerged as a pro…
Smart But Not Moral? Moral Alignment In Human-AI Decision-Making
Christiane Ernst, Luis Gutmann, Domenique Zipperling +2
In high-stakes AI-supported decisions, considerations are not purely technical but involve moral judgments about fairness, responsibility, and harm. While prior research has focuse…
It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Domenique Zipperling, Luca Deck, Julia Lanzl +1
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fa…
A Multivocal Literature Review on Privacy and Fairness in Federated Learning
Beatrice Balbierer, Lukas Heinlein, Domenique Zipperling +1
Federated Learning presents a way to revolutionize AI applications by eliminating the necessity for data sharing. Yet, research has shown that information can still be extracted du…
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
Domenique Zipperling, Simeon Allmendinger, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approach…