# Gender Bias in LLMs: Preliminary Evidence from Shared Parenting Scenario in Czech Family Law

arXiv: https://arxiv.org/abs/2601.05879 · HTML: https://mlsanity.com/paper/2601.05879/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 87 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 27 of 27)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools](https://mlsanity.com/paper/2405.20362/) (2024)
- [Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models](https://mlsanity.com/paper/2401.01301/) (2024)
- [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025)
- [On the Emergence of Position Bias in Transformers](https://mlsanity.com/paper/2502.01951/) (2025)
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://mlsanity.com/paper/2212.02199/) (2022)
- [Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration](https://mlsanity.com/paper/2310.09241/) (2023)
- [Blind Judgement: Agent-Based Supreme Court Modelling With GPT](https://mlsanity.com/paper/2301.05327/) (2023)
- [Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction](https://mlsanity.com/paper/2307.08321/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction](https://mlsanity.com/paper/2310.11761/) (2023)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Objection Overruled! Lay People can Distinguish Large Language Models from Lawyers, but still Favour Advice from an LLM](https://mlsanity.com/paper/2409.07871/) (2024)
- [Evaluating Position Bias in Large Language Model Recommendations](https://mlsanity.com/paper/2508.02020/) (2025)
- [Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers](https://mlsanity.com/paper/2311.06534/) (2023)
- [Automation Bias in the AI Act: On the Legal Implications of Attempting to De-Bias Human Oversight of AI](https://mlsanity.com/paper/2502.10036/) (2025)
- [Getting in the Door: Streamlining Intake in Civil Legal Services with Large Language Models](https://mlsanity.com/paper/2410.03762/) (2024)
- [NyayaRAG: Realistic Legal Judgment Prediction with RAG under the Indian Common Law System](https://mlsanity.com/paper/2508.00709/) (2025)
- [Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction](https://mlsanity.com/paper/2409.07055/) (2024)
- [Experimenting with Legal AI Solutions: The Case of Question-Answering for Access to Justice](https://mlsanity.com/paper/2409.07713/) (2024)
- [Prompting from the bench: Large-scale pretraining is not sufficient to prepare LLMs for ordinary meaning analysis](https://mlsanity.com/paper/2510.25356/) (2025)
- [LLMs & Legal Aid: Understanding Legal Needs Exhibited Through User Queries](https://mlsanity.com/paper/2501.01711/) (2025)
- [Large Language Models and Explainable Law: a Hybrid Methodology](https://mlsanity.com/paper/2311.11811/) (2023)
- [Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model](https://mlsanity.com/paper/2505.04132/) (2025)

## Built on by (most cited first, 1 of 1)

- [Algorithmic Authority and the Clinical Standard of Care](https://mlsanity.com/paper/2606.00044/) (2026)

## Abstract (arXiv)

Access to justice remains limited for many people, leading laypersons to increasingly rely on Large Language Models (LLMs) for legal self-help. Laypeople use these tools intuitively, which may lead them to form expectations based on incomplete, incorrect, or biased outputs. This study examines whether leading LLMs exhibit gender bias in their responses to a realistic family law scenario. We present an expert-designed divorce scenario grounded in Czech family law and evaluate four state-of-the-art LLMs GPT-5 nano, Claude Haiku 4.5, Gemini 2.5 Flash, and Llama 3.3 in a fully zero-shot interaction. We deploy two versions of the scenario, one with gendered names and one with neutral labels, to establish a baseline for comparison. We further introduce nine legally relevant factors that vary the factual circumstances of the case and test whether these variations influence the models' proposed shared-parenting ratios. Our preliminary results highlight differences across models and suggest gender-dependent patterns in the outcomes generated by some systems. The findings underscore both the risks associated with laypeople's reliance on LLMs for legal guidance and the need for more robust evaluation of model behavior in sensitive legal contexts. We present exploratory and descriptive evidence intended to identify systematic asymmetries rather than to establish causal effects.