Machine Learning Fairness for Depression Detection Using EEG Data
Machine Learning Fairness for Depression Detection Using EEG Data
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.
- University of Cambridge United Kingdom
- Middle East Technical University Turkey
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Depression, Computer Vision and Pattern Recognition (cs.CV), FOS: Clinical medicine, Data Science, Computer Science - Computer Vision and Pattern Recognition, Neurosciences, Bioengineering, Mental Illness, Machine Learning (cs.LG), Brain Disorders, Mental Health, Networking and Information Technology R&D (NITRD), 46 Information and Computing Sciences, 4611 Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Mental health, Electrical Engineering and Systems Science - Signal Processing
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Depression, Computer Vision and Pattern Recognition (cs.CV), FOS: Clinical medicine, Data Science, Computer Science - Computer Vision and Pattern Recognition, Neurosciences, Bioengineering, Mental Illness, Machine Learning (cs.LG), Brain Disorders, Mental Health, Networking and Information Technology R&D (NITRD), 46 Information and Computing Sciences, 4611 Machine Learning, FOS: Electrical engineering, electronic engineering, information engineering, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD), Mental health, Electrical Engineering and Systems Science - Signal Processing
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