Unpacking Human and AI Complementarity: Insights from Recent Works

被引：3

作者：

Ren Y. ^{[1
]}

Deng X.N. ^{[2
]}

Joshi K.D. ^{[3
]}

机构：

[1] University of Minnesota, Twin Cities, Minneapolis, MN

[2] California State University, Dominguez Hills, Carson, CA

[3] University of Nevada, Reno, NV

来源：

Data Base Adv. Info. Sys. | 2023年 / 3卷 / 6-10期

关键词：

ai-human complementarity; artificial intelligence; human skills; human-ai augmentation; machine learning;

D O I：

10.1145/3614178.3614180

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. © 2023 Copyright is held by the owner/author(s).

引用

页码：6 / 10

页数：4