32 citations · 72 across the 16 of their papers we have counts for
11 papers · 1 filter
From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer +2
High-quality labeled data is essential for training robust machine learning models, yet obtaining annotations at scale remains expensive. AI-assisted annotation has therefore becom…
When Thinking Pays Off: Incentive Alignment for Human-AI Collaboration
Joshua Holstein, Patrick Hemmer, Gerhard Satzger +1
Collaboration with artificial intelligence (AI) has improved human decision-making across various domains by leveraging the complementary capabilities of humans and AI. Yet, humans…
Development of Mental Models in Human-AI Collaboration: A Conceptual Framework
Joshua Holstein, Gerhard Satzger
Artificial intelligence has become integral to organizational decision-making and while research has explored many facets of this human-AI collaboration, the focus has mainly been…
Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration
Philipp Spitzer, Joshua Holstein, Katelyn Morrison +3
Across various applications, humans increasingly use black-box artificial intelligence (AI) systems without insight into these systems' reasoning. To counter this opacity, explaina…
Transferring Domain Knowledge with (X)AI-Based Learning Systems
Philipp Spitzer, Niklas Kühl, Marc Goutier +2
In numerous high-stakes domains, training novices via conventional learning systems does not suffice. To impart tacit knowledge, experts' hands-on guidance is imperative. However,…
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…