Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data

2025-11-12

AI Summary

This study investigates the dynamic evolution of gender bias during recursive synthetic data generation by large language models (LLMs). We find that bias does not monotonically amplify across generations but instead converges toward a dynamic equilibrium determined by the model’s intrinsic bias: low initial bias amplifies by 36% on average, whereas high initial bias attenuates by 26%. To mitigate this, we propose a contrastive augmentation–based debiasing strategy, achieving an average 91% reduction in gender bias on downstream tasks (up to 98.8%). Methodologically, we introduce a novel three-dimensional evaluation framework—comprising rule-based matching, embedding similarity, and downstream fairness—which reveals a significant misalignment between semantic similarity and fairness metrics. Our results demonstrate that bias evolution in synthetic data is inherently non-monotonic, underscoring the necessity of purpose-built recursive generation strategies to ensure data fairness.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Investigating gender bias dynamics in synthetic data generation across multiple recursive generations
• Evaluating bias mitigation strategies using multidimensional assessment frameworks
• Addressing the paradox between semantic metrics and behavioral fairness outcomes

Innovation

• Recursive prompting enables scalable synthetic dataset generation
• Contrastive augmentation introduces gender-swapped variants for mitigation
• Multidimensional evaluation reveals bias equilibrium dynamics