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

2024-06-06

AI Summary

Current AI research on speech for mental health and neurological disorders suffers from data bias, underrepresentation, and ethical oversight—undermining model trustworthiness and clinical applicability. This paper systematically evaluates existing speech datasets through a literature review, interdisciplinary ethical analysis, and data governance framework design, proposing— for the first time—a structured, ethics-driven guideline for constructing sensitive clinical speech data. Key contributions include: (1) identifying 21 critical ethical risk points across the full data lifecycle—acquisition, annotation, sharing, and privacy protection; (2) translating abstract ethical principles into a verifiable, actionable open-source checklist; and (3) establishing foundational principles for speech data curation that jointly ensure fairness, diversity, and accountability. The guideline addresses a methodological gap in the ethical governance of sensitive medical speech data and provides practical, implementation-ready support for deploying trustworthy AI in clinical settings.

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

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Fairness, accountability, transparency and ethics of web technologies

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.

Problem

• Addressing biases in speech datasets
• Promoting ethical AI in mental health
• Enhancing dataset diversity and fairness

Innovation

• Ethical speech dataset checklist
• Fairness and diversity focus
• Mental health data improvement