Gender and Racial Fairness in Depression Research using Social Media

Carlos Aguirre, Keith Harrigian, Mark Dredze


Abstract
Multiple studies have demonstrated that behaviors expressed on online social media platforms can indicate the mental health state of an individual. The widespread availability of such data has spurred interest in mental health research, using several datasets where individuals are labeled with mental health conditions. While previous research has raised concerns about possible biases in models produced from this data, no study has investigated how these biases manifest themselves with regards to demographic groups in data, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups. We find that model performance differs for underrepresented groups, and we investigate sources of these biases beyond data representation. Our study results in recommendations on how to avoid these biases in future research.
Anthology ID:
2021.eacl-main.256
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2932–2949
Language:
URL:
https://aclanthology.org/2021.eacl-main.256
DOI:
10.18653/v1/2021.eacl-main.256
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.256.pdf