Modeling Techniques for Machine Learning Fairness: A Survey
- URL: http://arxiv.org/abs/2111.03015v1
- Date: Thu, 4 Nov 2021 17:17:26 GMT
- Title: Modeling Techniques for Machine Learning Fairness: A Survey
- Authors: Mingyang Wan, Daochen Zha, Ninghao Liu, Na Zou
- Abstract summary: In recent years, various techniques have been developed to mitigate the bias for machine learning models.
In this survey, we review the current progress of in-processing bias mitigation techniques.
- Score: 17.925809181329015
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine learning models are becoming pervasive in high-stakes applications.
Despite their clear benefits in terms of performance, the models could show
bias 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 bias
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 bias 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.
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