  Machine learning models are becoming pervasive in high-stakes applications.
Despite their clear benefits in terms of performance, the models could show
discrimination against minority groups and result in fairness issues in a
decision-making process, leading to severe negative impacts on the individuals
and the society. In recent years, various techniques have been developed to
mitigate the unfairness for machine learning models. Among them, in-processing
methods have drawn increasing attention from the community, where fairness is
directly taken into consideration during model design to induce intrinsically
fair models and fundamentally mitigate fairness issues in outputs and
representations. In this survey, we review the current progress of
in-processing fairness mitigation techniques. Based on where the fairness is
achieved in the model, we categorize them into explicit and implicit methods,
where the former directly incorporates fairness metrics in training objectives,
and the latter focuses on refining latent representation learning. Finally, we
conclude the survey with a discussion of the research challenges in this
community to motivate future exploration.
