Challenges of Explaining the Behavior of Black-Box AI Systems

被引：60

作者：

Asatiani, Aleksandre ^{[1
]}

Malo, Pekka ^{[2
]}

Nagb, Per Radberg ^{[3
]}

Penttinen, Esko ^{[4
]}

Rinta-Kahila, Tapani ^{[5
]}

Salovaara, Antti ^{[6
]}

机构：

[1] Univ Gothenburg, Informat Syst, Dept Appl Informat Technol, Gothenburg, Sweden

[2] Aalto Univ, Sch Business, Stat, Espoo, Finland

[3] It Univ Copenhagen, Copenhagen, Denmark

[4] Aalto Univ, Sch Business, Informat Syst, Espoo, Finland

[5] Univ Queensland, Australian Inst Business & Econ, Brisbane, Qld, Australia

[6] Aalto Univ, Sch Arts Design & Architecture, Espoo, Finland

来源：

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

关键词：

D O I：

10.17705/2msqe.00037

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：

There are many examples of problems resulting from inscrutable AI systems, so there is a growing need to be able to explain how such systems produce their outputs. Drawing on a case study at the Danish Business Authority, we provide a framework and recommendations for addressing the many challenges of explaining the behavior of black-box AI systems. Our findings will enable organizations to successfully develop and deploy AI systems without causing legal or ethical problems.(1,2)

引用

页码：259 / 278

页数：20

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