首次评估脑电图抑郁检测中的机器学习公平性，发现数据与算法均存偏见。
Machine Learning Fairness for Depression Detection using EEG Data
- 对比CNN、LSTM、GRU在三个脑电数据集上的表现
- 五种去偏策略在不同公平性指标上效果各异
- 适合关注医疗AI公平性的研究者与临床应用开发者
本文首次系统评估了基于脑电图（EEG）数据进行抑郁检测时的机器学习公平性。我们在Mumtaz、MODMA和Rest三个EEG数据集上，采用卷积神经网络（CNN）、长短期记忆网络（LSTM）和门控循环单元（GRU）等深度学习模型开展实验，并在预处理、训练中和后处理阶段应用五种不同的偏见缓解策略，评估其有效性。结果表明，现有EEG数据集和算法中存在显著偏见，不同去偏方法在各类公平性度量下作用层次不同，需根据具体场景选择适配策略。
原文摘要 · Abstract (English)
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.
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