Introduction to AI Fairness

被引：0

作者：

Zhang, Yunfeng ^{[1
]}

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

Liao, Q. Vera ^{[1
]}

Singh, Moninder ^{[1
]}

机构：

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

来源：

EXTENDED ABSTRACTS OF THE 2021 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS (CHI'21) | 2021年

关键词：

algorithmic fairness; bias; decision support; discrimination-aware machine learning;

D O I：

10.1145/3411763.3444998

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Today, AI is used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased and making questionable and unfair decisions. Recently, the AI research community has proposed many methods to measure and mitigate unwanted biases, and developed open-source toolkits for developers to make fair AI. This course will cover the recent development in algorithmic fairness, including the many different definitions of fairness, their corresponding quantitative measurements, and ways to mitigate biases. This course is open to beginners and is designed for anyone interested in the topic of AI fairness.

引用

页数：3

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