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

TL;DR

This paper evaluates the ability of GPT-3 to generate synthetic mental health data across demographics, analyzing its representation of stressors and comparing it to human data to assess fidelity and limitations.

Contribution

It introduces HEADROOM, a new synthetic dataset of depression stressors across demographics, and provides methods to analyze and compare synthetic and human-generated mental health data.

Findings

Synthetic data partially replicates human data distribution of stressors.

GPT-3 can generate demographic-specific depression stressors.

Analysis reveals limitations in LLMs for fully capturing demographic nuances.

Abstract

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.