# Gender Bias in Depression Detection Using Audio Features

arXiv: https://arxiv.org/abs/2010.15120 · HTML: https://mlsanity.com/paper/2010.15120/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2020.

## Built on by (most cited first, 21 of 21)

- [Multi-modal Depression Estimation based on Sub-attentional Fusion](https://mlsanity.com/paper/2207.06180/) (2022)
- [DAIC-WOZ: On the Validity of Using the Therapist's prompts in Automatic Depression Detection from Clinical Interviews](https://mlsanity.com/paper/2404.14463/) (2024)
- [HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection](https://mlsanity.com/paper/2408.03648/) (2024)
- [U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection](https://mlsanity.com/paper/2501.09687/) (2025)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China](https://mlsanity.com/paper/2408.04026/) (2024)
- [Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Prediction](https://mlsanity.com/paper/2406.08183/) (2024)
- [A knowledge-driven vowel-based approach of depression classification from speech using data augmentation](https://mlsanity.com/paper/2210.15261/) (2022)
- [Domain Adversarial Training for Mitigating Gender Bias in Speech-based Mental Health Detection](https://mlsanity.com/paper/2505.03359/) (2025)
- [Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research](https://mlsanity.com/paper/2406.04116/) (2024)
- [Investigating the Sensitivity of Pre-trained Audio Embeddings to Common Effects](https://mlsanity.com/paper/2501.15900/) (2025)
- [Fair Uncertainty Quantification for Depression Prediction](https://mlsanity.com/paper/2505.04931/) (2025)
- [Assessing Algorithmic Bias in Language-Based Depression Detection: A Comparison of DNN and LLM Approaches](https://mlsanity.com/paper/2509.25795/) (2025)
- [Integration of Text and Graph-based Features for Detecting Mental Health Disorders from Voice](https://mlsanity.com/paper/2205.07006/) (2022)
- [Analyzing the Effect of Data Impurity on the Detection Performances of Mental Disorders](https://mlsanity.com/paper/2308.05133/) (2023)
- [Exploring the Task-agnostic Trait of Self-supervised Learning in the Context of Detecting Mental Disorders](https://mlsanity.com/paper/2403.15170/) (2024)
- [Class Unbiasing for Generalization in Medical Diagnosis](https://mlsanity.com/paper/2508.06943/) (2025)
- [Systematic FAIRness Assessment of Open Voice Biomarker Datasets for Mental Health and Neurodegenerative Diseases](https://mlsanity.com/paper/2508.14089/) (2025)
- [Mitigating Gender Bias in Depression Detection via Counterfactual Inference](https://mlsanity.com/paper/2512.01834/) (2025)
- [DepFlow: Disentangled Speech Generation to Mitigate Semantic Bias in Depression Detection](https://mlsanity.com/paper/2601.00303/) (2026)
- ["OK Aura, Be Fair With Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection](https://mlsanity.com/paper/2604.05830/) (2026)

## Abstract (arXiv)

Depression is a large-scale mental health problem and a challenging area for machine learning researchers in detection of depression. Datasets such as Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) have been created to aid research in this area. However, on top of the challenges inherent in accurately detecting depression, biases in datasets may result in skewed classification performance. In this paper we examine gender bias in the DAIC-WOZ dataset. We show that gender biases in DAIC-WOZ can lead to an overreporting of performance. By different concepts from Fair Machine Learning, such as data re-distribution, and using raw audio features, we can mitigate against the harmful effects of bias.