Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for an instance of a task or to delegate it to a human by considering both parties' capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams -- compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when they are aware that an AI model delegated task instances to them. 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. Additionally, we identify humans' increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces.
Computational ergonomics for task delegation in Human-Robot Collaboration: spatiotemporal adaptation of the robot to the human through contactless gesture recognition
Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes
Tandon School of Engineering New York University（纽约大学工程学院）；Stern School of Business New York University（纽约大学商学院）
A human-centered approach to reframing job satisfaction in the BIM-enabled construction industry
Gerald May Dept. of Civil, Construction and Environmental Engineering, University of New Mexico（新墨西哥大学）；School of Architecture and Planning, University of New Mexico（新墨西哥大学）
Collaboration with Conversational AI Assistants for UX Evaluation: Questions and How to Ask them (Voice vs. Text)
Rochester Institute of Technology（罗切斯特理工学院）；University of Waterloo（滑铁卢大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）
CANote: Empowering Fact-checking Note Writing Through Scaffolded and Provenance-based Human-AI Collaboration
Tsinghua University（清华大学）；Cornell University（康奈尔大学）；University of Luxembourg（卢森堡大学）；College of Design and Innovation, Tongji University（同济大学设计创意学院）；University of Washington（华盛顿大学）
