Generating synthetic candidate profiles that mimic real data distributions without revealing any actual personal information is increasingly used in training recruitment models. This aids in addressing bias and improving inclusivity by enabling model development on diverse datasets while sidestepping privacy issues related to handling real candidate data.
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