Think Your Artificial Intelligence Software Is Fair? Think Again

被引：21

作者：

Bellamy, Rachel K. E. ^{[1
]}

Dey, Kuntal ^{[2
]}

Hind, Michael ^{[1
]}

Hoffman, Samuel C. ^{[1
]}

Houde, Stephanie ^{[1
]}

Kannan, Kalapriya ^{[3
]}

Lohia, Pranay ^{[3
]}

Mehta, Sameep ^{[3
]}

Mojsilovic, Aleksandra ^{[4
]}

Nagar, Seema ^{[5
]}

Ramamurthy, Karthikeyan Natesan ^{[1
]}

Richards, John ^{[1
]}

Saha, Diptikalyan ^{[3
]}

Sattigeri, Prasanna ^{[1
]}

Singh, Moninder ^{[1
]}

Varshney, Kush R. ^{[1
]}

Zhang, Yunfeng ^{[1
]}

机构：

[1] IBM Res, Yorktown Hts, NY 10598 USA

[2] IBM Res, New Delhi, India

[3] IBM Res, Bangalore, Karnataka, India

[4] IBM Res, AI Fdn, Yorktown Hts, NY USA

[5] IBM Res, Artificial Intelligence, Bangalore, Karnataka, India

来源：

IEEE SOFTWARE | 2019年 / 36卷 / 04期

关键词：

Application contexts - Machine learning software;

D O I：

10.1109/MS.2019.2908514

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Today, machine-learning software is used to help make decisions that affect people's lives. Some people believe that the application of such software results in fairer decisions because, unlike humans, machine-learning software generates models that are not biased. Think again. Machine-learning software is also biased, sometimes in similar ways to humans, often in different ways. While fair model- assisted decision making involves more than the application of unbiased models-consideration of application context, specifics of the decisions being made, resolution of conflicting stakeholder viewpoints, and so forth-mitigating bias from machine-learning software is important and possible but difficult and too often ignored. © 1984-2012 IEEE.

引用

页码：76 / 80

页数：5

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