PEEC: The Protected Entities Ethics Checklist for Collecting Speech Data From Vulnerable Clinical Populations. - Choi, Anna Seo Gyeong; Cho, Sunghye; Nowenstein, Iris (Journal of Speech, Language & Hearing Research, 2026)
Purpose: The rapid advancement of automatic speech recognition (ASR) and natural language processing technologies has created significant opportunities for clinical applications within speech and language disorders, yet these capabilities remain largely confined to high-resource languages and populations. As research communities work to address these inequities through inclusive speech data collection, the intersection of clinical vulnerability, linguistic diversity, and emerging speech and language technologies creates ethical considerations that are rarely addressed by existing guidelines. Ethical data collection practices affect the fairness and bias profiles of automatic speech and language analysis systems trained on these data, creating a foundational link between participant protection and algorithmic justice. Method: This article introduces the Protected Entities Ethics Checklist (PEEC), a comprehensive framework specifically designed for researchers collecting speech and language data from populations requiring enhanced protections. The framework addresses three core domains: participant protection and consent, data collection standards, and compliance implementation. Critically, the PEEC situates ethical data collection as a prerequisite for developing fair ASR systems, recognizing that procedural justice in research must precede algorithmic fairness. Results: The PEEC framework provides structured guidance for ethical research with protected entities including children, elderly adults with cognitive changes, individuals with communication disorders, and marginalized communities. It offers population-specific consent mechanisms, enhanced data protection measures, systematic quality assurance procedures, and explicit guidance on technical considerations for ASR applications while maintaining flexibility for diverse research contexts. Conclusions: Ethical treatment of research participants is inextricably linked to algorithmic fairness in speech technology development. The PEEC framework argues that procedural justice in data collection is a prerequisite for achieving fair AI systems, establishing the necessary ethical foundation for subsequent technological development in clinical speech research. By ensuring equitable and respectful data collection practices, we create the foundation for ASR systems that perform equitably across diverse populations.