Gender Disparities in LLM-Based Intimate Partner Violence Detection

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Social Sciences & Behavioral Studies · Depth: Expert, medium

Summary

The study by Prama et al. examines gender disparities in how large language models (LLMs) detect Intimate Partner Violence (IPV). Researchers utilized 475 Reddit posts from r/relationship_advice, generating counterfactual variants by swapping gendered identifiers to create four victim-perpetrator dyads: female-female (F/F), female-male (F/M), male-female (M/F), and male-male (M/M). Four recent LLMs, specifically GPT-5o, Gemini 3, Llama 4, and Grok 3, evaluated these variants using a structured questionnaire covering IPV, perpetrator intent, cheating, and abuse subtypes. Results showed substantial variation across models and dyads. Abuse and intent detection systematically decreased in mixed-gender dyads where the victim is male, with female perpetrator identity consistently predicting lower abuse recognition. Mixed-effects logistic regression confirmed that gender roles significantly shape model outputs, indicating LLMs reproduce gendered biases from their online training data.

Key takeaway

For AI Scientists and NLP Engineers deploying LLMs in sensitive domains like intimate partner violence detection, you must rigorously test for gender biases. Your models, including GPT-5o, Gemini 3, Llama 4, and Grok 3, may under-recognize abuse when the victim is male and the perpetrator is female, reflecting biases from training data. Implement counterfactual testing with varied gender configurations to identify and mitigate these disparities before deployment, ensuring equitable and accurate support.

Key insights

LLMs exhibit gender bias in IPV detection, under-recognizing abuse when the victim is male and the perpetrator is female.

Principles

• LLMs reproduce training data biases.
• Gender roles shape model outputs.
• Victim-perpetrator gender impacts detection.

Method

Counterfactual variants of 475 Reddit posts were created by swapping gender identifiers. Four LLMs (GPT-5o, Gemini 3, Llama 4, Grok 3) evaluated these variants using a structured questionnaire.

In practice

• Test LLMs with diverse gender configurations.
• Audit models for gender-based detection disparities.
• Consider training data bias in sensitive domains.

Topics

• LLM Bias
• Intimate Partner Violence
• Gender Disparities
• Counterfactual Testing
• Natural Language Processing
• Social Computing

Best for: Research Scientist, AI Product Manager, AI Scientist, NLP Engineer, AI Ethicist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.