February 10, 2020
Public Interest Research in Data Management and Machine Learning
With over 80% of the planet impacted by urbanization, metropolitan areas have become loci of influence socially, economically, and politically. As cities grow in size and complexity, the broad use of automated decision systems appears inevitable; as some argue, AI is the new electricity. But the data that power these systems are deeply flawed — incomplete, incorrect, inaccessible, and systematically biased. As a result, decision systems trained naively on opportunistic data sources will, at best, calcify the status quo and at worst operationalize and legitimize the wrong answers.
In this talk, I’ll describe our work to address these issues through a multi-institutional, multi-disciplinary, and use-inspired research agenda in responsible urban analytics. I’ll also describe a specific line of technical work on the EquiTensors project, where we are facilitating access to heterogeneous collections of urban datasets while adjusting for fairness and privacy. As building blocks, we have developed algorithms for repairing data using causal models, incorporating fairness into spatio-temporal models, learning joint embeddings for multi-modal datasets (i.e., images and text), and applying differential privacy to large-scale, complex datasets.
I’ll end with some thoughts on the importance of city-university collaborations in advancing basic research, training students, and engaging on critical societal issues.
Bill Howe is Associate Professor in the Information School and Adjunct Associate Professor in the Allen School of Computer Science & Engineering and the Department of Electrical Engineering. His research interests are in data management, machine learning, and visualization, particularly as applied in the physical and social sciences. As Founding Associate Director of the UW eScience Institute, Dr. Howe played a leadership role in the Moore-Sloan Data Science Environment program through a $32.8 million grant awarded jointly to UW, NYU, and UC Berkeley, and founded UW’s Data Science for Social Good Program. With support from the MacArthur Foundation, NSF, and Microsoft, Howe directs UW’s participation in the Cascadia Urban Analytics Cooperative. He founded the UW Data Science Masters Degree, serving as its inaugural Program Chair, and created a first MOOC on data science that attracted over 200,000 students. His research has been featured in the Economist and Nature News, and he has authored award-winning papers in conferences across data management, machine learning, and visualization. He has a Ph.D. in Computer Science from Portland State University and a Bachelor’s degree in Industrial & Systems Engineering from Georgia Tech.