@inproceedings{kattamuri-etal-2026-equilibrium,
    title = "Equilibrium Dynamics and Mitigation of Gender Bias in Synthetically Generated Data",
    author = "Kattamuri, Ashish  and
      Vats, Arpita  and
      Fartale, Harshwardhan  and
      Raja, Rahul  and
      Moharir, Akshata Kishore  and
      Prasad, Ishita",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.4/",
    pages = "37--42",
    ISBN = "979-8-89176-424-8",
    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."
}