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Fairness or algorithmic fairness refers to different approaches to removing algorithmic bias from machine learning models. The process of data synthesization can be used to fix biases embedded in the data via upsampling minority groups, such as high earning women in a dataset. The challenge in creating fair algorithms is that fairness needs to be defined on a case-by-case basis, considering the social and economic context in all its complexity.

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