图表
资历
知识管理
生产力
知识工作者
计算机科学
人力资源
心理学
管理
工作(物理)
工程类
统计
经济
数学
航空航天工程
宏观经济学
机械工程
作者
Weiguang Wang,Guodong Gao,Ritu Agarwal
出处
期刊:Social Science Research Network
[Social Science Electronic Publishing]
日期:2019-01-01
被引量:1
摘要
As AI applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience, narrow experience based on the specific task volume and broad experience based on seniority, on the human-AI team dynamics. We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers. Based on a detailed analysis performed at the medical chart level, we find evidence that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues. Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se, but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities. This study provides new empirical insights into the differential roles of worker experience in the collaborative dynamics between AI and knowledge workers, which have important societal and business implications.
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