Reinforcing intensive motherhood: A study of gender bias in parental responsibilities allocation by large language models

TL;DR

This study shows that large language models like GPT-4.1 and DeepSeek-V3 assign more caregiving responsibilities to mothers than fathers, reinforcing traditional gender roles in parenting.

Contribution

The study extends LLM bias research to the domestic domain of childrearing, revealing how models amplify traditional gender norms.

Findings

Both models assigned highest caregiving responsibility scores to mothers and lowest to fathers.

LLMs showed higher responsibility scores in prescriptive contexts, reflecting normative social expectations.

Gender equality attitudes did not explain the bias, suggesting it stems from training data associations.

Abstract

This study investigated gender bias in Large Language Models (LLMs) within the context of parenting responsibility attribution, focusing on whether LLMs implicitly reinforce the ideology of “intensive mothering” by assigning caregiving duties predominantly to mothers. Using GPT-4.1 and DeepSeek-V3 as case studies, we used a 3-factor experimental design involving model type, caregiver role (mother, father, or neutral parent), and responsibility framing (prescriptive vs. descriptive). Results revealed an obvious gender bias across both models: mothers were consistently assigned highest caregiving responsibility scores, while fathers received the lowest. Moreover, LLMs produced higher responsibility scores in prescriptive contexts than in descriptive ones, suggesting a tendency to reflect normative social expectations. Mediation analysis showed that gender equality attitudes did not significantly explain these biases, indicating that LLMs’ outputs were likely driven by contextual associations in training data rather than consistent ideological positioning. This study extended LLMs bias research into the domestic domain of childrearing, highlighting that even in private contexts, advanced language models tend to reproduce and amplify traditional gender norms. The findings underscored the urgency of incorporating gender sensitivity in LLMs design and training processes. Interventions such as fine-tuning and dataset balancing are essential to prevent these models from reinforcing gendered divisions of labor in parenting.

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