Annisaa Fitri Nurfirdausi
abstract
As artificial intelligence becomes increasingly integrated into mental health diagnostics, ensuring
fairness and transparency is critical—particularly in sensitive applications such as depression detection.
This project explores fairness-aware deep learning using raw EEG signals from the MODMA (Multi-
modal Open Dataset for Mental-disorder Analysis) dataset [1]. Three deep neural architectures are
developed and evaluated: a Convolutional Neural Network (CNN) as a baseline model, a CNN combined
with Long Short-Term Memory (CNN-LSTM), and a CNN combined with Gated Recurrent Units and
Attention mechanism (CNN-GRU-Attention). To address potential biases, five mitigation techniques are
applied across three stages: preprocessing, in-processing, and post-processing. The models are evaluated
using both performance metrics (accuracy, precision, recall, F1-score) and fairness metrics (disparate
impact, statistical parity, equal opportunity, average odds, and equalized accuracy).
outcomes