Roles of Artificial Intelligence in Collaboration with Humans: Automation, Augmentation, and the Future of Work
研究了在判断任务中，AI替代人类（自动化）或辅助人类（增强）的最优方式，发现任务间互补性促进自动化，任务内互补性促进增强，并通过图像分类实验验证了最优工作配置模式。
Humans will see significant changes in the future of work as collaboration with artificial intelligence (AI) will become commonplace. This work explores the benefits of AI in the setting of judgment tasks when it replaces humans (automation) and when it works with humans (augmentation). Through an analytical modeling framework, we show that the optimal use of AI for automation or augmentation depends on different types of human-AI complementarity. Our analysis demonstrates that the use of automation increases with higher levels of between-task complementarity. In contrast, the use of augmentation increases with higher levels of within-task complementarity. We integrate both automation and augmentation roles into our task allocation framework, where an AI and humans work on a set of judgment tasks to optimize performance with a given level of available human resources. We validate our framework with an empirical study based on experimental data in which humans classify images with and without AI support. When between-task complementarity and within-task complementarity exist, we see a consistent distribution of work pattern for optimal work configurations; AI automates relatively easy tasks, AI augments humans on tasks with similar human and AI performance, and humans work without AI on relatively difficult tasks. Our work provides several contributions to theory and practice. The findings on the effects of complementarity provide a nuanced view regarding the benefits of automation and augmentation. Our task allocation framework highlights potential job designs for the future of work, especially by considering the often-ignored, critical role of human resource reallocation in improving organizational performance. This paper was accepted by D. J. Wu, Special Issue on the Human-Algorithm Connection. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.05684 .