Title: Toward Trustworthy AI for Mental Healthcare: Exploring Socio-Demographic Bias, Privacy Risks, and Collaborative Decision-Making

Presenter: Dr. Theodora Chaspari, Associate Professor, ICS and Department of Computer Science, University of Colorado Boulder

Abstract: The rise in accessibility of smartphones and wearable devices has revolutionized the monitoring of human states beyond laboratory settings. This advancement has resulted in the collection of real-life, multimodal, temporal data, forming a valuable basis for developing machine learning (ML) algorithms that track an individual's internal and contextual states, holding great promise for enhancing mental healthcare. Simultaneously, the relationship between humans and AI has evolved into a collaborative dynamic, where humans and AI systems work together towards common objectives. However, several challenges, both technical and societal, hinder the widespread adoption of such technologies. This talk will discuss the concept of trust in human-centered AI. Specifically, it will explore concerns related to unintentional disclosure of personal information when using multi-modal bio-behavioral signals in ML systems, and investigate potential socio-demographic biases that arise from these systems. Additionally, the talk will present findings from decision-making tasks that involve human clinicians collaborating with explainable AI systems to estimate human states and mental health outcomes.

Bio: Theodora Chaspari is an Associate Professor in Computer Science and the Institute of Cognitive Science at University of Colorado Boulder. She has received a B.S. (2010) in Electrical & Computer Engineering from the National Technical University of Athens, Greece and M.S. (2012) and Ph.D. (2017) in Electrical Engineering from the University of Southern California (USC). She was a Research Assistant at the Signal Analysis and Interpretation Laboratory at USC (2010-2017) and an Assistant Professor at Texas A&M University (2017-2023). Theodora’s research interests lie in human-centered machine learning, affective computing, and biomedical health informatics. She is a recipient of the TEES Dean of Engineering Excellence Award (2022), NSF CAREER Award (2021), and USC Women in Science and Engineering Merit Fellowship (2015). Papers co-authored with her students have been nominated and won awards at the ACM BuildSys 2019, IEEE ACII 2019, ASCE i3CE 2019, and IEEE BSN 2018 conferences. She is serving as an Editor of the Elsevier Computer Speech & Language. Her work is supported by federal and private funding sources, including the NSF, NIH, NASA, IARPA, AFOSR, General Motors, and the Engineering Information Foundation.