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20242026
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cs.HC2026

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…

cs.HC2026

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…

cs.HC2025

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…

cs.HC2025

AI Reliance and Decision Quality: Fundamentals, Interdependence, and the Effects of Interventions

Jakob Schoeffer, Johannes Jakubik, Michael Voessing +2

In AI-assisted decision-making, a central promise of having a human-in-the-loop is that they should be able to complement the AI system by overriding its wrong recommendations. In…

cs.HC2025

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…

cs.HC2025

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…