Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data

TL;DR

This paper studies how gender bias evolves in synthetic data generated by large language models through recursive prompting, revealing equilibrium dynamics and effective mitigation strategies like contrastive augmentation.

Contribution

It uncovers the equilibrium behavior of gender bias in recursive generation and evaluates mitigation strategies, emphasizing the importance of multidimensional bias assessment.

Findings

Bias can amplify or decay toward the model's inherent bias level.

Contrastive augmentation significantly reduces downstream bias.

Semantic similarity metrics may not align with fairness outcomes.

Abstract

Recursive prompting with large language models enables scalable synthetic dataset generation but introduces the risk of bias amplification. We investigate gender bias dynamics across three generations of recursive text generation using three complementary evaluation frameworks: rule-based pattern matching, embedding-based semantic similarity, and downstream task performance. Experiments with three initial bias levels (0.1, 0.3, 0.6) and four mitigation strategies reveal equilibrium dynamics rather than monotonic amplification. The low initial bias amplifies toward the model's inherent bias level (+36%), whereas the high initial bias decays toward it (-26%). Among mitigation methods, contrastive augmentation, which introduces gender-swapped variants, achieves significant downstream bias reduction (98.8% for low initial bias and 91% on average) despite producing higher embedding-based bias scores. This paradox demonstrates that semantic similarity metrics may diverge from behavioral fairness outcomes, highlighting the need for multidimensional evaluation in responsible synthetic data generation.