Unpacking Human and AI Complementarity: Insights from Recent Works

IF 2.8 4区 管理学 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE Data Base for Advances in Information Systems Pub Date : 2023-07-31 DOI:10.1145/3614178.3614180
Yuqing Ren, X. Deng, K. D. Joshi
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Abstract

In this editorial, we draw insights from recent empirical studies to answer some key questions related to human and AI complementarity. There is consensus regarding the strengths of machine intelligence in performing structured and codifiable tasks and complementing two human limitations: lack of consistency and inability to unlearn conventional wisdom. To work effectively with AI, humans need to possess not only AI skills but also domain expertise, job skills, and metaknowledge to accurately assess human capabilities and AI capabilities. We identify several future directions in understanding the effects of human expertise and experiences on algorithmic appreciation, the mutual learning and adaptions between humans and AI, and the boundary conditions of effective human and AI complementarity.
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解开人类和人工智能的互补性:来自近期作品的见解
在这篇社论中,我们从最近的实证研究中汲取见解,以回答与人类和人工智能互补性相关的一些关键问题。关于机器智能在执行结构化和可编码任务方面的优势,以及补充人类的两个局限性,人们达成了共识:缺乏一致性和无法忘记传统智慧。为了有效地与人工智能合作,人类不仅需要掌握人工智能技能,还需要掌握领域专业知识、工作技能和元知识,以准确评估人类能力和人工智能能力。我们确定了未来的几个方向,以理解人类专业知识和经验对算法欣赏的影响,人类和人工智能之间的相互学习和适应,以及人类和人工智能有效互补的边界条件。
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来源期刊
Data Base for Advances in Information Systems
Data Base for Advances in Information Systems INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
3.60
自引率
7.10%
发文量
18
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