Keynote: Co-Design of a Trustworthy AI-driven Voice-based System in Mental Health Monitoring

Recent research confirms that acoustic features extracted from speech are valid indicators for assessing the severity of manic and depressive symptoms. While several applications currently support the monitoring of individuals with affective disorders-such as MONARCA, MoodMon, ReMind, SIMBA, and UP!-these systems typically do not provide explanations for their reasoning or decisions. To address this limitation, we propose the development of an explainable AI-driven, voice-based system for mental health monitoring. This system is designed to generate explanations regarding how a person's voice speaking has changed across different affective states. The co-design process has been conducted by an interdisciplinary team of experts and stakeholders comprising engineers, technical experts, psychiatrists, legal scholars, ethicists, philosophers, and other specialists. The trustworthiness of the proposed AI system is evaluated using the Z-Inspection® methodology. This methodology involves investigation of the relevant ethical standards in the development, implementation and use of artificial intelligence systems and also consideration of the socio-technical context in which the system will be used. During the presentation, we will share first insights and lessons learned from the trustworthiness assessment and co-design process of the proposed system.