Gender Disparities in LLM-Based Intimate Partner Violence Detection
For developers and deployers of LLMs in sensitive support contexts, this work highlights a critical fairness issue that could lead to unequal treatment of IPV victims based on gender.
This study investigates whether LLMs exhibit gender bias in detecting intimate partner violence (IPV) from Reddit posts, finding that abuse detection systematically decreases when the victim is male and the perpetrator is female, with gender roles significantly shaping model outputs.
Intimate Partner Violence (IPV) is a major public health concern, and large language models (LLMs) are increasingly used for support and information-seeking in sensitive domains. We examine whether LLMs perceive relationship abuse differently depending on victim--perpetrator gender configuration. Using 475 Reddit posts from r/relationship\_advice, we generate counterfactual variants by swapping gendered identifiers to create four dyads: female--female (F/F), female--male (F/M), male--female (M/F), and male--male (M/M), where the first position denotes the victim. Four recent LLMs (GPT-5o, Gemini 3, Llama 4, and Grok 3) evaluate each variant using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results show substantial variation across models and dyads. Abuse and intent detection systematically decrease in mixed-gender dyads where the victim is male, with female perpetrator identity emerging as a consistent negative predictor of abuse recognition. Mixed-effects logistic regression confirms that gender roles significantly shape model outputs. Our findings suggest that LLMs reproduce gendered biases from online training data, with implications for support-related deployment. Code and resources are available at https://github.com/TabiaTanzin/Gender-Disparities-in-LLM-Based-Intimate-Partner-Violence-Detection.git.