A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

2024-12-30

AI Summary

This study addresses diagnostic unfairness in AI-based screening for childhood anxiety disorders arising from linguistic gender bias. We propose the first data-centric debiasing framework specifically designed for pediatric mental health texts. Methodologically, we identify non-biological gender biases by analyzing linguistic distributional disparities in clinical texts—such as information density and lexical preferences—and introduce a word-level neutralization rewriting strategy that preserves critical clinical terminology while eliminating bias at the language representation level, without modifying model architecture. Our key contribution lies in targeting linguistic representation—not model adaptation—to jointly ensure clinical fidelity and fairness. Experiments demonstrate a 9% reduction in misdiagnosis rates for adolescent girls, a 27% decrease in overall gender-based diagnostic disparity, and significant improvements in cross-gender accuracy and outcome parity—evidenced by reduced false negative rate (FNR) disparities. The framework offers an interpretable, reproducible debiasing paradigm for equitable deployment of mental health AI systems.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.

Problem

• Gender Bias
• AI Models
• Child Anxiety Assessment

Innovation

• Gender Bias Mitigation
• AI Mental Health Screening
• Debiasing Technique