Building Data Equity Systems

Abstract

This session is part of the T-TRIPODS Seminar Series on Making Real-World Data Science Responsible Data Science.

Equity as a social concept — treating people differently depending on their endowments and needs to provide equality of outcome rather than equality of treatment — lends a unifying vision for ongoing work to operationalize ethical considerations across technology, law, and society. In my talk I will present a vision for designing, developing, deploying, and overseeing data-intensive systems that consider equity as an essential requirement. I will discuss ongoing technical work in scope of the "Data, Responsibly" project, and will place this work into the broader context of policy, education, and public outreach activities.

Bio:

Julia Stoyanovich is an Institute Associate Professor of Computer Science & Engineering at the Tandon School of Engineering, Associate Professor of Data Science at the Center for Data Science, and Director of the Center for Responsible AI at New York University (NYU). Her research focuses on responsible data management and analysis: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data science lifecycle. She established the "Data, Responsibly" consortium and served on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. Julia developed and has been teaching courses on Responsible Data Science at NYU, and is a co-creator of an award-winning comic book series on this topic. In addition to data ethics, Julia works on the management and analysis of preference and voting data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics & Statistics from the University of Massachusetts at Amherst. She is a recipient of an NSF CAREER award and a Senior Member of the ACM.

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Meeting ID: 971 8312 0811

Passcode: See colloquium email