Designing computational tools for behavioral and clinical science

Automatic analysis of human affective and social signals brought computer science into closer alignment with social sciences, enabling new collaborations between computer scientists and behavioral researchers. In this talk, I highlight the key research directions in this burgeoning interdisciplinary field, and provide an overview of its major opportunities and challenges. 

 

Computer science and psychology have different methodological assumptions and approaches. In the extended version of this talk, I introduce the foundational principles of both computational and statistical methods used in this context. I explain the core concepts, outline different goals and methodologies, and distinguish between data-driven and theory-driven approaches.

 

Drawing on examples from our recent research - such as automatic analysis of interactive play therapy sessions with children, and diagnosis of bipolar disorder from multimodal cues - as well as relying on examples from the growing literature, I explore the potential of human-AI collaboration, where AI systems do not replace, but support monitoring and human decision making in behavioral and clinical sciences. In particular, the role of face, body, gesture, speech and multimodal analysis  are discussed, as well the role of explainability and interpretability, which are important aspects for trustworthy computer systems in this domain.