Statistical parity is one possible definition of fairness in ML, which adjusts the data so that decisions are made fairly without discrimination. The goal is to ensure the same probability of inclusion in the positive predicted class for each sensitive group. An example is that women and men are equally likely to be promoted at work, and neither group is at an advantage or disadvantage because of gender. In general, synthetic data contains the same bias in the data that is present in the original data. However, the generation of synthetic data can be adjusted to meet the definition of statistical parity.
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