The study found that the BDI-Sen dataset used for depression symptom detection on social media exhibits gender bias, with machine learning models such as mentalBERT showing predictive disparities that generally favour male users. Although bias mitigation techniques like data augmentation reduced the bias, they did not eliminate it completely.
The existence of this bias affects the fairness and reliability of AI systems in detecting depression symptoms, leading to unequal predictive performance across genders. This can result in under- or over-identification of depression symptoms in certain groups, thereby compromising the validity of such systems for clinical or mental health monitoring.
Learn more about this study here: https://studenttheses.uu.nl/handle/20.500.12932/47734
Reference
Gierschmann, Lara (2024), The Role of Gender: Gender Fairness in the Detection of Depression Symptoms on Social Media, Utrecht University, unpublished Master Thesis