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The fairness of machine learning (ML) approaches is critical to thereliability of modern artificial intelligence systems. Despite extensive studyon this topic, the fairness of ML models in the software engineering (SE)domain has not been well explored yet. As a result, many ML-powered softwaresystems, particularly those utilized in the software engineering community,continue to be prone to fairness issues. Taking one of the typical SE tasks,i.e., code reviewer recommendation, as a subject, this paper conducts the firststudy toward investigating the issue of fairness of ML applications in the SEdomain. Our empirical study demonstrates that current state-of-the-art ML-basedcode reviewer recommendation techniques exhibit unfairness and discriminatingbehaviors. Specifically, male reviewers get on average 7.25% morerecommendations than female code reviewers compared to their distribution inthe reviewer set. This paper also discusses the reasons why the studiedML-based code reviewer recommendation systems are unfair and provides solutionsto mitigate the unfairness. Our study further indicates that the existingmitigation methods can enhance fairness by 100% in projects with a similardistribution of protected and privileged groups, but their effectiveness inimproving fairness on imbalanced or skewed data is limited. Eventually, wesuggest a solution to overcome the drawbacks of existing mitigation techniquesand tackle bias in datasets that are imbalanced or skewed.

A First Look at Fairness of Machine Learning Based Code Reviewer Recommendation