Promoting the Responsible Development of Speech Datasets for Mental Health and Neurological Disorders Research

TL;DR

This paper reviews speech datasets used in mental health and neurological research, emphasizing ethical considerations, fairness, and diversity to promote responsible AI development in sensitive healthcare domains.

Contribution

It provides a comprehensive overview of existing datasets, highlights ethical pitfalls, and offers a checklist for responsible and fair dataset creation.

Findings

Identifies biases and limitations in current speech datasets.

Proposes ethical guidelines and a checklist for dataset development.

Highlights the importance of diversity and fairness in healthcare AI data.

Abstract

Current research in machine learning and artificial intelligence is largely centered on modeling and performance evaluation, less so on data collection. However, recent research demonstrated that limitations and biases in data may negatively impact trustworthiness and reliability. These aspects are particularly impactful on sensitive domains such as mental health and neurological disorders, where speech data are used to develop AI applications for patients and healthcare providers. In this paper, we chart the landscape of available speech datasets for this domain, to highlight possible pitfalls and opportunities for improvement and promote fairness and diversity. We present a comprehensive list of desiderata for building speech datasets for mental health and neurological disorders and distill it into an actionable checklist focused on ethical concerns to foster more responsible research.