# Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data

arXiv: https://arxiv.org/abs/2403.16909 · HTML: https://mlsanity.com/paper/2403.16909/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 33 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Large Language Models are Few-Shot Clinical Information Extractors](https://mlsanity.com/paper/2205.12689/) (2022)
- [Does Synthetic Data Generation of LLMs Help Clinical Text Mining?](https://mlsanity.com/paper/2303.04360/) (2023)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [Differentially Private Language Models for Secure Data Sharing](https://mlsanity.com/paper/2210.13918/) (2022)
- [Gendered Mental Health Stigma in Masked Language Models](https://mlsanity.com/paper/2210.15144/) (2022)
- [Gender and Racial Fairness in Depression Research using Social Media](https://mlsanity.com/paper/2103.10550/) (2021)
- [Using Noisy Self-Reports to Predict Twitter User Demographics](https://mlsanity.com/paper/2005.00635/) (2020)

## Built on by (most cited first, 4 of 4)

- [Thousand Voices of Trauma: A Large-Scale Synthetic Dataset for Modeling Prolonged Exposure Therapy Conversations](https://mlsanity.com/paper/2504.13955/) (2025)
- [Roleplaying with Structure: Synthetic Therapist-Client Conversation Generation from Questionnaires](https://mlsanity.com/paper/2510.25384/) (2025)
- [Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data](https://mlsanity.com/paper/2511.07044/) (2025)
- [Chatting Up Attachment: Using LLMs to Predict Adult Bonds](https://mlsanity.com/paper/2409.00347/) (2024)

## Abstract (arXiv)

Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demographics are represented in it. In our paper, we analyze the potential of producing synthetic data using GPT-3 by exploring the various stressors it attributes to different race and gender combinations, to provide insight for future researchers looking into using LLMs for data generation. Using GPT-3, we develop HEADROOM, a synthetic dataset of 3,120 posts about depression-triggering stressors, by controlling for race, gender, and time frame (before and after COVID-19). Using this dataset, we conduct semantic and lexical analyses to (1) identify the predominant stressors for each demographic group; and (2) compare our synthetic data to a human-generated dataset. We present the procedures to generate queries to develop depression data using GPT-3, and conduct analyzes to uncover the types of stressors it assigns to demographic groups, which could be used to test the limitations of LLMs for synthetic data generation for depression data. Our findings show that synthetic data mimics some of the human-generated data distribution for the predominant depression stressors across diverse demographics.