Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data

arXiv — cs.CL•Monday, November 17, 2025 at 5:00:00 AM

The paper titled 'Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data' explores the dynamics of gender bias in synthetic datasets generated through recursive prompting with large language models. It examines three initial bias levels (0.1, 0.3, 0.6) and evaluates four mitigation strategies. The findings indicate that low initial bias amplifies by 36%, while high initial bias decays by 26%. Notably, contrastive augmentation significantly reduces downstream bias by 98.8% for low initial bias and 91% on average, despite higher embedding-based bias scores.

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