Fairness in Machine Learning for Healthcare

被引：41

作者：

Ahmad, Muhammad Aurangzeb ^{[1
,2
]}

Patel, Arpit ^{[3
]}

Eckert, Carly ^{[2
,4
]}

Kumar, Vikas ^{[2
]}

Teredesai, Ankur ^{[1
,2
]}

机构：

[1] Univ Washington Tacoma, Dept Comp Sci, Tacoma, WA 98402 USA

[2] KenSci Inc, Seattle, WA 98101 USA

[3] Univ Washington, Dept Bioinformat & Med Educ, Seattle, WA 98195 USA

[4] Univ Washington, Dept Epidemiol, Seattle, WA 98195 USA

来源：

KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING | 2020年

关键词：

healthcare ai; machine learning in healthcare; fairness; fatml; fate ml; DISPARITIES;

D O I：

10.1145/3394486.3406461

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The issue of bias and fairness in healthcare has been around for centuries. With the integration of AI in healthcare the potential to discriminate and perpetuate unfair and biased practices in healthcare increases many folds. The tutorial focuses on the challenges, requirements and opportunities in the area of fairness in healthcare AI and the various nuances associated with it. The problem healthcare as a multi-faceted systems level problem that necessitates careful consideration of different notions of fairness in healthcare to corresponding concepts in machine learning is elucidated via different real world examples.

引用

页码：3529 / 3530

页数：2

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