Abstract

There is an inevitable gap between computational systems, which follow principles of precision, formalization, and abstraction, and human needs in the social world, which are contextual, fluid, and nuanced. Making AI technologies more responsible and human-centered requires understanding and bridging this socio-technical gap. Focusing on my research on explainable AI (XAI), and more recently large language models (LLMs), I will discuss four approaches my collaborators and I take to bridge the socio-technical gap for emerging and consequential AI technologies: conducting critical investigations into dominant AI algorithmic paradigms; researching and supporting AI practitioners who address the socio-technical gap on the ground; developing socially-guided computation and design frameworks; and shaping technical development by embedding socio-technical perspectives in the evaluation practices. I will conclude the talk with future directions toward my long-term research agenda of informing and developing AI technologies that are beneficial and adaptable for different stakeholder groups and usage contexts.  

Q. Vera Liao is a Principal Researcher at Microsoft Research Montréal, where she is part of the FATE (Fairness, Accountability, Transparency, and Ethics in AI) group. Her current research interests are in human-AI interaction, explainable AI, and responsible AI, with an overarching goal of bridging emerging AI technologies and human-centered perspectives. Prior to joining MSR, she worked at IBM Research, and studied at the University of Illinois at Urbana-Champaign and Tsinghua University. She has authored more than 60 peer-reviewed research articles and received many paper awards at HCI and AI venues. She currently serves as the co-editor-in-chief for the Springer HCI Book Series, in the Editors team for ACM CSCW conference, and on the Editorial Board of ACM TiiS. She has also served as an Area Chair and Senior PC member for CHI, FAccT, and IUI conferences.