Addressing the Key Challenges of Developing Machine Learning AI Systems for Knowledge-Intensive Work

被引：19

作者：

Zhang, Zhewei ^{[1
]}

Nandhakumar, Joe ^{[2
]}

Hummel, Jochem Thomas ^{[1
]}

Waardenburg, Lauren ^{[3
]}

机构：

[1] Univ Warwick, Warwick Business Sch, Informat Syst Management Grp, Coventry, W Midlands, England

[2] Univ Warwick, Warwick Business Sch, Informat Syst, Coventry, W Midlands, England

[3] Vrije Univ Amsterdam, Sch Business & Econ, KIN Ctr Digital Innovat, Amsterdam, Netherlands

来源：

MIS QUARTERLY EXECUTIVE | 2020年 / 19卷 / 04期

基金：

英国科研创新办公室; “创新英国”项目;

关键词：

D O I：

10.17705/2msqe.00035

中图分类号：

G25 [图书馆学、图书馆事业]; G35 [情报学、情报工作];

学科分类号：

1205 ; 120501 ;

摘要：

Al has great potential to change the way businesses operate. However, developers o f Al systems face many challenges because of the significant differences compared to traditional systems. We describe how a machine learning Al system for legal practice was developed to help legal professionals make faster and better-informed decisions. We identified three key challenges encountered by developers and users and, based on their experience, provide actions and recommendations for addressing the challenges of developing machine learning Al systems for knowledge-intensive work.(1,2)

引用

页码：221 / 238

页数：18

共 19 条

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[7] Brynjolfsson E., 2017, Harvard Business Review
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