Evaluating and Designing Computing Systems for the Future of Work | Hancheng Cao
From collaborative software to generative AI, computing technologies are redefining the way we work, communicate and collaborate. Yet with the growing complexities of computing platforms, it becomes increasingly challenging to foresee their impacts on human behavior, leading to not only poor user experience but also problematic applications that mirror and amplify societal issues. How can we better understand machine behavior and machine-mediated user behavior over computing platforms? How can we build applications that align with our needs and values with emerging computing technologies? My research aims to answer these questions through novel measurements and computational methods inspired by social science insights, such as mining increasingly available large-scale data on how people build, adopt, and interact with computing systems. In this talk, I will present my work demonstrating this approach in the future of work context, where I develop data-driven, AI-powered and human-centered methods to understand, evaluate and design sociotechnical systems at the workplace. I will present an analysis of remote meeting experience through mining millions of meetings, a study on how an AI algorithm can be built to predict team fracture, and a development and evaluation study on a generative AI-based scientific feedback system for researchers. These projects exemplify the opportunities to leverage computation and data to better understand, support and augment work practices.