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2022-23 Morison Prize and Lecture with danah boyd, PhD – “Made, Not Found: Grappling with the Vulnerabilities of Data”
May 8 @ 4:00 pm - 5:30 pm
From danah boyd’s website:
danah boyd is a Partner Researcher at Microsoft Research and a Distinguished Visiting Professor at Georgetown University. Her research focuses on the intersection of technology and society, with an eye to how structural inequities shape and are shaped by technologies. She is currently conducting a multi-year ethnographic study of the US census to understand how data are made legitimate. Her previous studies have focused on media manipulation, algorithmic bias, privacy practices, social media, and teen culture. Her monograph “It’s Complicated: The Social Lives of Networked Teens” has received widespread praise. She founded the research institute Data & Society, where she currently serves as an advisor. She is also a member of the Council on Foreign Relations, and on the advisory board of Electronic Privacy Information Center. She received a bachelor’s degree in computer science from Brown University, a master’s degree from the MIT Media Lab, and a Ph.D in Information from the University of California, Berkeley.
About the talk:
Made, Not Found: Grappling with the Vulnerabilities of Data
The U.S. census is a piece of data infrastructure upon which countless programs, policies, and decisions depend. In fact, many data produced in the 21st century ripples through complex sociotechnical systems, shaping actions far from the point of data production and collection. This is particularly visible when it comes to the development of artificial intelligence systems. By understanding how data are made, we can start to appreciate the various work that goes into ensuring that data are resilient.
In this talk, danah will draw on lessons learned studying the construction of 2020 U.S. census data to grapple with the ways in which political forces shape data in order to shape the systems that depend on those data. This talk will weave through discussions of differential privacy, statistical repairwork, and epistemic contestations about what makes data “real” to showcase the invisible layers of data that we all take for granted.