Gender and Racial Fairness in Depression Research using Social Media
This addresses fairness issues in mental health research for underrepresented demographic groups, though it is incremental as it builds on prior concerns about biases.
The study quantified gender and racial biases in depression classifiers trained on Twitter data, finding systematic performance differences for underrepresented groups that are not fully explained by data representation issues.
Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status. The widespread availability of such data has spurred interest in mental health research from a computational lens. While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, 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 systematically differs for underrepresented groups and that these discrepancies cannot be fully explained by trivial data representation issues. Our study concludes with recommendations on how to avoid these biases in future research.