Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China

TL;DR

This study evaluates how gender and cultural differences affect the fairness of multimodal depression prediction models using datasets from the USA and China, highlighting the need for culturally aware data collection.

Contribution

First comprehensive analysis of multimodal gender fairness in depression detection across different cultures using multiple algorithms and datasets.

Findings

Differences observed between datasets, but causality remains unclear.

Results suggest external factors may influence data disparities.

Calls for culturally sensitive data collection practices.

Abstract

Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The problem of bias and fairness in ML algorithms is becoming an increasingly greater source of concern. In concurrence, existing literature has also indicated that mental health conditions can manifest differently across genders and cultures. We hypothesise that the representation of features (acoustic, textual, and visual) and their inter-modal relations would vary among subjects from different cultures and genders, thus impacting the performance and fairness of various ML models. We present the very first evaluation of multimodal gender fairness in depression manifestation by undertaking a study on two different datasets from the USA and China. We undertake thorough statistical and ML experimentation and repeat the experiments for several different algorithms to ensure that the results are not algorithm-dependent. Our findings indicate that though there are differences between both datasets, it is not conclusive whether this is due to the difference in depression manifestation as hypothesised or other external factors such as differences in data collection methodology. Our findings further motivate a call for a more consistent and culturally aware data collection process in order to address the problem of ML bias in depression detection and to promote the development of fairer agents and robots for wellbeing.