Machine Learning Fairness for Depression Detection using EEG Data
This addresses fairness issues in mental health diagnostics for patients, but it is incremental as it applies known methods to a new domain.
The paper tackled the problem of machine learning fairness in depression detection using EEG data by evaluating bias in existing datasets and algorithms, and found that different bias mitigation methods address bias at varying levels across fairness measures.
This paper presents the very first attempt to evaluate machine learning fairness for depression detection using electroencephalogram (EEG) data. We conduct experiments using different deep learning architectures such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks across three EEG datasets: Mumtaz, MODMA and Rest. We employ five different bias mitigation strategies at the pre-, in- and post-processing stages and evaluate their effectiveness. Our experimental results show that bias exists in existing EEG datasets and algorithms for depression detection, and different bias mitigation methods address bias at different levels across different fairness measures.