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

arXiv:2403.16909v1 Announce Type: cross
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 …

abstract applications arxiv cs.ai cs.cl cs.cy data demographics domains fidelity generated health however human impact mental health paper representation synthetic synthetic data tasks type understanding