Fairness Metrics of Machine Learning:Review of Status,Challenges and Future Directions
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 compre-hensive 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.
Machine learningFairness of machine learningTrust-worthy artificial intelligenceFairness metricsFair decision
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