Human-Centered Artificial Intelligence for Mental Health
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Abstract: Artificial intelligence (AI) offers promising solutions for advancing prevention, diagnosis, and intervention in mental health. However, many approaches face challenges in terms of real-world usability and interpretability. In this talk, I will outline our human-centered approach to AI for mental health, with a focus on developing algorithms and systems that work in practical settings. Specifically, I will present our work on multimodal automated detection of psychosocial stress, integrating computer vision, remote photoplethysmography, and behavioral signal processing to capture this key risk factor for mental health. Furthermore, I will discuss two computational approaches to support psychiatric diagnosis: (1) a multimodal video-based analysis of social interactions, and (2) a network-based analysis of messaging patterns. Finally, I will address the crucial role of explainability in AI for mental health, detailing current challenges in interpreting affective models and proposing methods to improve model trustworthiness for clinical use.
Bio: Hanna Drimalla is a Professor of Human-Centered Artificial Intelligence at the Center for Cognitive Interaction Technology (CITEC) at Bielefeld University. Combining methods from computer science and psychology, her research focuses on automated stress measurement, computational analysis of social interaction patterns and explainablity. In 2019, she received her Ph.D. from Humboldt-Universität zu Berlin, focusing on mimicry and empathy analyzed with methods from psychophysiology and machine learning. As a postdoc, she worked on digital mental health at the Hasso-Plattner Institute at the University of Potsdam. Since 2020, she has led a junior research group on empathic artificial intelligence and the Multimodal Behavior Processing Group at Bielefeld University.