Elamur Research⌕
☰
home / papers / 2026 / Gender Disparities in LLM-Based Intimate Partner Violence Detection

Gender Disparities in LLM-Based Intimate Partner Violence Detection

Paper · 2026 · Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker et al.

Summary

The paper asks whether large language models judge the same abuse story differently depending on the genders of victim and abuser. This matters because people seeking help with intimate partner violence (IPV) increasingly turn to LLM tools. The authors manually selected 475 two-person narratives from r/relationship_advice that had clear victim and perpetrator roles and stated genders. They then programmatically rewrote pronouns, kinship terms, names and gender tags to produce every victim/perpetrator configuration (F/F, F/M, M/F, M/M), giving 1,900 texts.

GPT-5o, Gemini 3, Llama 4 and Grok 3 each completed a structured questionnaire grounded in IPV frameworks. It covered relationship status, IPV presence, controlling intent, infidelity and six abuse types. No gold labels exist, so the authors compare positive-label rates (PLR) and fit mixed-effects logistic regressions with a random intercept per post.

The models differ widely in baseline sensitivity. All of them flag abuse less often when the perpetrator is a woman, so male victims of female partners are recognized least. This gendered double standard argues for bias audits before LLMs are used in survivor support.

Key points

  • Dataset: The original posts were skewed toward mixed-gender dyads (29 F/F, 228 F/M, 202 M/F, 16 M/M). Counterfactual swapping placed every post in all four dyads (475 × 4 = 1,900 samples).
  • Outcomes: The ten labels were IS_REL, IS_IPV, HAS_INTENT, IS_CHEATING, IS_EMOT, IS_PSYC, IS_PHYS, IS_SEXL, IS_FINL and IS_TECH. For the three-way questions, only explicit "yes" answers counted as positive.
  • Llama 4 shows the largest gender sensitivity:
    • IPV detection falls from 40.08% (F/F) to 30.59% (M/F).
    • Intent attribution falls from 39.87% to 21.73%, a drop of about 18 points.
    • GPT-5o and Grok 3 are much more conservative overall, flagging IPV in roughly 12–20% of cases.
  • Regression results:
    • A female perpetrator significantly lowers the odds of an IS_IPV "yes" for every model. GPT-5o shows the strongest effect (OR = 0.504).
    • Female–female dyads produce a compensating boost, most strikingly for Llama 4 on IS_TECH (OR = 9.316).
  • Limitations:
    • The sample is small.
    • Only four proprietary models were tested.
    • All queries used a fixed temperature of 1.0.
    • There is no expert-annotated ground truth.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of VermontBurlington · United States6 works · 3,740 citations🎓The University of AdelaideAdelaide · Australia6 works · 1,417 citations🎓Adelaide UniversityAdelaide · Australia5 works · 3 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSocial Sciences · 1,772 worksHealth · 5 works

Cite

Cite as Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, Christopher M. Danforth and Peter Dodds. 2026. Gender Disparities in LLM-Based Intimate Partner Violence Detection. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.nlpcss-1.13

@inproceedings{prama2026gender,
  title = {{Gender Disparities in LLM-Based Intimate Partner Violence Detection}},
  author = {Tabia Tanzin Prama, Mikaela Irene Fudolig, Abigail M. Crocker, Christopher M. Danforth and Peter Dodds},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.13},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.13}
}

References

35 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Meta AI. 2025. Llama 4: Multimodal intelligence. Accessed: 2025. find
  2. Matthew Breiding, Michele C. Black, and George W. Ryan. 2008. Chronic disease and health risk behaviors associated with intimate partner violence-18 u.s. states/territories, 2005. Annals of epidemiology, 18 7:538–44. find
  3. Aw-M Choi, Bc-Y Lo, Rt-F Lo, Py-L To, and Jy-H Wong. 2021. Intimate partner violence victimization, social support, and resilience: Effects on the anxiety levels of young mothers. Journal of Interpersonal Violence, 36(21–22):NP12299–NP12323. 2023. Bias and fairness in large language models: A survey…
  4. D. Gilbert and E. B. Postel. 2021. Truth without trauma: Reducing re-traumatization throughout the justice system. University of Louisville Law Review, 60. find
  5. K. Hegarty, L. Tarzia, J. Valpied, E. Murray, C. Humphreys, A. Taft, and 1 others. 2019. An online healthy relationship tool and safety decision aid for women experiencing intimate partner violence (i-decide): A randomised controlled trial. The Lancet Public Health, 4(6):e301–e310. find
  6. Lori Heise and Claudía Garcia-Moreno. 2002. Violence by intimate partners. find
  7. Denise A. Hines and Emily M. Douglas. 2016. Relative influence of various forms of partner violence on the health of male victims: Study of a helpseeking sample. Psychology of men & masculinity, 17 1:3– 16. find
  8. V. Hui, R. E. Constantino, and Y. J. Lee. 2023. Harnessing machine learning in tackling domestic violence— an integrative review. International Journal of Environmental Research and Public Health, 20(6):4984. find
  9. V. Hui, B. Zhang, B. Jeon, K. C. A. Wong, M. L. Klem, and Y. J. Lee. 2024. Harnessing health information technology in domestic violence in the united states: A scoping review. Public Health Reviews, 45:1606654. find
  10. B Kim and Av Merlo. 2023. Domestic homicide: A synthesis of systematic review evidence. Trauma, Violence, & Abuse, 24(2):776–793. find
  11. J. Koziol-McLain, A. C. Vandal, D. Wilson, S. Nada-Raja, T. Dobbs, and C. McLean. 2018. Efficacy of a web-based safety decision aid for women experiencing intimate partner violence: Randomized controlled trial. Journal of Medical Internet Research, 20(1):e8. find
  12. Google DeepMind. 2024. Gemini: A family of highly capable multimodal models.
  13. Tai Pong Lam, H. Y. Chan, Leon Piterman, Samuel Y. S. Wong, K. F. Lam, and K. S. Sun. 2020. Factors that facilitate recognition and management of domestic violence by primary care physicians in a chinese context: A mixed methods study in hong kong. BMC Family Practice, 21:155. find
  14. N Ennis, I Sijercic, and Cm Monson. 2021. Trauma-focused cognitive-behavioral therapies for posttraumatic stress disorder under ongoing threat: A systematic review. Clinical Psychology Review, 88:102049. find
  15. Andreia Machado, Denise A. Hines, and Marlene Matos. 2016. Help-seeking and needs of male victims of intimate partner violence in portugal. Psychology of Men and Masculinity, 17:255–264. find
  16. M. Ford-Gilboe, C. Varcoe, K. Scott-Storey, N. Perrin, J. Wuest, C. N. Wathen, and 1 others. 2020. Longitudinal impacts of an online safety and health intervention for women experiencing intimate partner violence: Randomized controlled trial. BMC Public Health, 20(1):260. find
  17. Wookjae Maeng and Joonhwan Lee. 2021. Designing a chatbot for survivors of sexual violence: Exploratory study for hybrid approach combining rule-based chatbot and ml-based chatbot. Proceedings of the Asian CHI Symposium 2021. find
  18. Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, Md Mehrab Tanjim, Sungchul Kim, Franck Dernoncourt, Tong Yu, Ruiyi Zhang, and Nesreen Ahmed. find
  19. Steve Mulligan. 2009. Redefining Domestic Violence: Using the Power and Control Paradigm for Domestic Violence Legislation. Children’s Legal Rights Journal, 29(1):33–43. find
  20. I. Naismith, K. Ripoll-Nuñez, and G. B. Henao. 2024. Depression, anxiety, and posttraumatic stress disorder following intimate partner violence: The role of self-criticism, guilt, and gender beliefs. Violence Against Women, 30(3–4):791–811. find
  21. S. S. Nayak, X. Efimov, C. N. Ncube, J. Griffith, and B. E. Molnar. 2023. “No Safe Spaces”: The retrauma-tization and dehumanization of immigrant survivors of domestic violence in the united states. Journal of Immigrant & Refugee Studies, 24(1):158–173. find
  22. Sihyun Park, Su-Hyang Bang, and Jae hee Jeon. 2020. “this society ignores our victimization”: Understanding the experiences of korean male victims of intimate partner violence. Journal of Interpersonal Violence, 36:11658 – 11680. find
  23. Cora Peterson, Megan Crawford Kearns, Wendy LiKamWa McIntosh, Lianne Fuino Estefan, Christina Nicolaidis, Kathryn E. Mccollister, Ariel D Gordon, and Curtis S. Florence. 2018. Lifetime economic burden of intimate partner violence among u.s. adults. American journal of preventive medicine, 55 4:433–4… find
  24. Tabia Tanzin Prama, Julia Witte Zimmerman, Christopher M. Danforth, and Peter Sheridan Dodds. 2025. Us-vs-them bias in large language models. Preprint, arXiv:2512.13699. which digital technologies facilitate youths’ access to services at intimate partner violence organizations. Children and Youth Se… — [arXiv:2512.13699](https://arxiv.org/abs/2512.13699) find
  25. US Department of Justice Office on Violence Against Women. 2025. Domestic Violence. https://www.justice.gov/ovw/domestic-violence. ↗
  26. Arlene Walker, Kimina Lyall, Dilkie Silva, Georgia Craigie, Richelle Mayshak, Beth M. Costa, Shannon Hyder, and Alexandra Bentley. 2020. Male victims of female-perpetrated intimate partner violence, help-seeking, and reporting behaviors: A qualitative study. Psychology of Men and Masculinity, 21:213… find
  27. World Health Organization. Violence against women. Fact sheet. Accessed 2026-02-27. find
  28. World Health Organization and London School of Hygiene and Tropical Medicine. 2010. Preventing Intimate Partner and Sexual Violence Against Women: Taking Action and Generating Evidence. World Health Organization, Geneva. find
  29. En Wright, A Hanlon, A Lozano, and Am Teitelman. 2021. The association between intimate partner violence and 30-year cardiovascular disease risk among young adult women. Journal of Interpersonal Violence, 36(11–12):NP6643–NP6660. xAI. 2025. Grok 3. find
  30. C. Rivas, C. Vigurs, J. Cameron, and L. Yeo. 2019. A realist review of which advocacy interventions work for which abused women under what circumstances. Cochrane Database of Systematic Reviews, 6(6):CD013135. find
  31. Renee A. Schneider, Mandi L. Burnette, Mark Andrew Ilgen, and Christine Timko. 2009. Prevalence and correlates of intimate partner violence victimization among men and women entering substance use disorder treatment. Violence and Victims, 24:744 – 756. find
  32. Aaditya K. Singh, Adam Fry, Adam Perelman, Adam Tart, Adithya Ganesh, Ahmed El-Kishky, Aidan McLaughlin, Aiden Low, AJ Ostrow, Akhila Ananthram, Akshay Nathan, Alan Luo, Alec Helyar, Aleksander Madry, Aleksandr A Efremov, Aleksandra Spyra, Alex Baker-Whitcomb, Alex Beutel, Alex Karpenko, and 464 oth… — OpenAI GPT-5 System Card
  33. Marudan Sivagurunathan, David M. Walton, Tara L. Packham, Richard G. Booth, and Joy Macdermid. 2021a. Discourses around male ipv related systemic biases on reddit. Journal of Interpersonal Violence, 37:NP17834 – NP17859. find
  34. Marudan Sivagurunathan, David M. Walton, Tara L. Packham, Richard G. Booth, and Joy Macdermid. 2021b. “punched in the balls”: Male intimate partner violence disclosures and replies on reddit. American Journal of Men’s Health, 15. find
  35. H. L. Storer, E. X. Nyerges, and S. Hamby. 2022. Technology “feels less threatening”: The processes by find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/prama-2026-gender-disparities-llm-based-intimate/