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

arXiv: https://arxiv.org/abs/2408.04026 · HTML: https://mlsanity.com/paper/2408.04026/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 67 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 17 of 17)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition](https://mlsanity.com/paper/1907.11510/) (2019)
- [Investigating Bias and Fairness in Facial Expression Recognition](https://mlsanity.com/paper/2007.10075/) (2020)
- [Deep Learning for Depression Recognition with Audiovisual Cues: A Review](https://mlsanity.com/paper/2106.00610/) (2021)
- [Gender Bias in Depression Detection Using Audio Features](https://mlsanity.com/paper/2010.15120/) (2020)
- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [Introducing Representations of Facial Affect in Automated Multimodal Deception Detection](https://mlsanity.com/paper/2008.13369/) (2020)
- [VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2312.09740/) (2023)
- [Small but Fair! Fairness for Multimodal Human-Human and Robot-Human Mental Wellbeing Coaching](https://mlsanity.com/paper/2407.01562/) (2024)
- [Bias Reducing Multitask Learning on Mental Health Prediction](https://mlsanity.com/paper/2208.03621/) (2022)
- [Modeling User Empathy Elicited by a Robot Storyteller](https://mlsanity.com/paper/2107.14345/) (2021)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [Appropriateness of LLM-equipped Robotic Well-being Coach Language in the Workplace: A Qualitative Evaluation](https://mlsanity.com/paper/2401.14935/) (2024)
- [Robots as Mental Well-being Coaches: Design and Ethical Recommendations](https://mlsanity.com/paper/2208.14874/) (2022)
- [Continual Learning for Affective Robotics: A Proof of Concept for Wellbeing](https://mlsanity.com/paper/2206.11354/) (2022)

## Built on by (most cited first, 7 of 7)

- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Gender Fairness of Machine Learning Algorithms for Pain Detection](https://mlsanity.com/paper/2506.11132/) (2025)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [FairSSL: Fair Multimodal Self-Supervised Learning](https://mlsanity.com/paper/2508.16748/) (2025)
- [Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement](https://mlsanity.com/paper/2509.02624/) (2025)
- [Set to Be Fair: Demographic Parity Constraints for Set-Valued Classification](https://mlsanity.com/paper/2510.04926/) (2025)
- [FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment](https://mlsanity.com/paper/2604.23786/) (2026)

## Abstract (arXiv)

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.