Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience

• 成果类型: Article
• 署名作者: Wang, Weiguang; Gao, Guodong (Gordon); Agarwal, Ritu
• 署名单位: University of Rochester; Johns Hopkins University
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.00588
• 发表日期: 2024
• 页码: 5753-5775
• 关键词: Artificial intelligence; Human-AI Teaming; worker experience; PRODUCTIVITY; Healthcare; medical coding

摘要

As artificial intelligence (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.