[1]DENG Wei,XING Yuhan,LI Yifan,et al.Survey on fair machine learning[J].CAAI Transactions on Intelligent Systems,2020,15(3):578-586.[doi:10.11992/tis.202007004]
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Survey on fair machine learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 3 Page number: 578-586 Column: 人工智能院长论坛 Public date: 2020-05-05
Title:
Survey on fair machine learning
Author(s):
DENG Wei1; 2;  XING Yuhan1;  LI Yifan1;  LI Zhenhua3;  WANG Guoyin2
1. Center of Statistical Research, Southwestern University of Finance and Economics, Chengdu 611130, China;
2. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
3. School of Finance, Southwestern University of Finance and Economics, Chengdu 611130, China
Keywords:
algorithmic ethics; algorithmic discrimination; fairness; fair machine learning; fair indicator; fair design; fair dataset; dynamicity
CLC:
TP181
DOI:
10.11992/tis.202007004
Abstract:
With the widespread applications of machine learning in our society, the problems of discrimination have caused widespread social controversy. It gradually arouses strong interests in fair machine learning in the industry and academia. Nowdays the deep understanding of the basic issues related to fairness and mechanism of fair machine learning is still in their infancy. We makes a survey on fair machine learning. Starting from the definitions of fairness, it compares the different difinitions on fairness in different problems. Common datasets are also summarized. And the issues of fairness is analyzed. We classify and compare the existing methods of achieving fairness. Finally, we summarizes the problems in current fairness machine learning research and propose the key problems and important challenges in the future.
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