# Machine Learning Fairness for Depression Detection using EEG Data

arXiv: https://arxiv.org/abs/2501.18192 · HTML: https://mlsanity.com/paper/2501.18192/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [FNNC: Achieving Fairness through Neural Networks](https://mlsanity.com/paper/1811.00247/) (2018)
- [MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis](https://mlsanity.com/paper/2002.09283/) (2020)
- [Gender and Racial Fairness in Depression Research using Social Media](https://mlsanity.com/paper/2103.10550/) (2021)
- [Uncertainty-based Fairness Measures](https://mlsanity.com/paper/2312.11299/) (2023)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)

## Built on by (most cited first, 7 of 7)

- [Gender Fairness of Machine Learning Algorithms for Pain Detection](https://mlsanity.com/paper/2506.11132/) (2025)
- [Automatic Depression Assessment using Machine Learning: A Comprehensive Survey](https://mlsanity.com/paper/2506.18915/) (2025)
- [FairSSL: Fair Multimodal Self-Supervised Learning](https://mlsanity.com/paper/2508.16748/) (2025)
- [FG 2025 TrustFAA: the First Workshop on Towards Trustworthy Facial Affect Analysis: Advancing Insights of Fairness, Explainability, and Safety (TrustFAA)](https://mlsanity.com/paper/2506.05095/) (2025)
- [AI Models for Depressive Disorder Detection and Diagnosis: A Review](https://mlsanity.com/paper/2508.12022/) (2025)
- [Who Owns The Robot?: Four Ethical and Socio-technical Questions about Wellbeing Robots in the Real World through Community Engagement](https://mlsanity.com/paper/2509.02624/) (2025)
- [FAIR_XAI: Improving Multimodal Foundation Model Fairness via Explainability for Wellbeing Assessment](https://mlsanity.com/paper/2604.23786/) (2026)

## Abstract (arXiv)

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.