ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (2): 264-280.doi: 10.7544/issn1000-1239.2021.20200758

Special Issue: 2021数据治理与数据透明专题

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Fairness Research on Deep Learning

Chen Jinyin1,2, Chen Yipeng2, Chen Yiming2, Zheng Haibin2, Ji Shouling3, Shi Jie4, Cheng Yao4   

  1. 1(Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou 310023);2(College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023);3(College of Computer Science and Technology, Zhejiang University, Hangzhou 310058);4(Huawei International Pte Ltd, Singapore 138589)
  • Online:2021-02-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (62072406), the Natural Science Foundation of Zhejiang Province (LY19F020025), and the Major Special Funding for “Science and Technology Innovation 2025” in Ningbo (2018B10063).

Abstract: Deep learning is an important field of machine learning research, which is widely used in industry for its powerful feature extraction capabilities and advanced performance in many applications. However, due to the bias in training data labeling and model design, research shows that deep learning may aggravate human bias and discrimination in some applications, which results in unfairness during the decision-making process, thereby will cause negative impact to both individuals and socials. To improve the reliability of deep learning and promote its development in the field of fairness, we review the sources of bias in deep learning, debiasing methods for different types biases, fairness measure metrics for measuring the effect of debiasing, and current popular debiasing platforms, based on the existing research work. In the end we explore the open issues in existing fairness research field and future development trends.

Key words: deep learning, algorithm fairness, debiasing method, fairness metric, machine learning

CLC Number: