Authors: J Beck, S Eckman, C Kern, F Kreuter
Year: 2025
Published in: arXiv preprint arXiv:2509.08514, 2025 - arxiv.org
Institution: National Institutes of Health, National Center for Biotechnology Information
Research Area: Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
Human attitudes toward AI strongly influence performance in collaborative tasks, with skeptics showing better error detection and accuracy, while automation favorability increases overreliance on AI suggestions.
Methods: Randomized experiment with a controlled annotation task manipulating AI suggestion quality, task burden, and performance-based financial incentives; collected demographic, attitudinal, and behavioral data.
Key Findings: Impact of AI suggestion quality, task burden, and financial incentives on participant performance metrics (accuracy, correction activity, overcorrection, undercorrection); influence of demographic and psychological characteristics on performance.
Citations: 4
Sample Size: 2784
Authors: J Beck
Year: 2025
Published in: 2025 - edoc.ub.uni-muenchen.de
Institution: Ludwig-Maximilians-Universität München, University of Bayreuth
Research Area: Annotation Quality, Human-AI Collaboration, Behavioral Science, Human-Computer Interaction (HCI)
Discipline: Human-Computer Interaction (HCI)
The study empirically evaluates annotation bias, proposes strategies to reduce its impact, and explores the use of large language models in automated and hybrid annotation workflows.
Methods: Empirical assessments and experimental evaluations involving annotation workflows and large language models.
Key Findings: Annotation bias, annotation quality, and the effectiveness of hybrid workflows integrating human input and AI models.