Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (1): 266-272.doi: 10.11896/jsjkx.230500224

• Artificial Intelligence • Previous Articles     Next Articles

Fairness Metrics of Machine Learning:Review of Status,Challenges and Future Directions

ZHANG Wenqiong, LI Yun   

  1. School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    Jiangsu Key Laboratory of Big Data Security and Intelligent Processing,Nanjing 210003,China
  • Received:2023-05-30 Revised:2023-09-27 Online:2024-01-15 Published:2024-01-12
  • About author:ZHANG Wenqiong,born in 1998,Ph.D candidate.Her main researchinterestis fairness of machine learning.
    LI Yun,born in 1974,Ph.D,professor,Ph.D supervisor.His main research interests include machine learning and pattern recognition.
  • Supported by:
    Natural Science Fund for Colleges and Universities in Jiangsu Province(21KJA520003).

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Abstract: With the increasing popularity of machine learning applications,fairness of machine learning has attracted widespread attention from academia and industry,and has become an important component of trust-worthy artificial intelligence.To evaluate and improve the fairness of machine learning applications,a series of fairness metrics have been proposed by researchers.These metrics help to ensure fair decision-making of machine learning models among different individuals and groups,and provide gui-dance for improving and optimizing the model.However,there is still no consensus on the difference and correlation between these metrics,which are not clearly divided in different scenarios and tasks.This means that these fairness metrics lack a comprehensive classification system.In this paper,the fairness metrics are comprehensively organized and classified.Starting from the mathematical definition of these metrics,they are divided into two categories according to whether they are based on probability statistics.The two types of metrics are then further divided and elaborated separately.In order to facilitate readers’ understan-ding and application,combined with a practical case,the advantages and challenges of various metrics are pointed out in terms of application scenarios and implementation conditions,and the relationship between metrics is also discussed in conjunction with mathematical concepts,and possible future research directions are prospected.

Key words: Machine learning, Fairness of machine learning, Trust-worthy artificial intelligence, Fairness metrics, Fair decision

CLC Number: 

  • TP391
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