Dr. Kentaro Toyama
W.K. Kellogg Professor of Community Information, School of Information, University of Michigan
Professor of Information, School of Information, University of Michigan
Beyond Algorithmic Fairness: What Social Justice Needs from Data Science
Abstract: In the last few years, the data science community has awoken to a range of ways in which data-driven decision-making can be biased, often perpetuating existing systems of discrimination. In response, a research community focused on the fairness, accountability, and transparency of big data systems has arisen remarkably quickly, raising and answering questions such as, How should “fairness” be defined? How can biased training data be de-biased? What machine learning algorithms allow intuitive interpretation of their outcomes? And so on. But, while these are essential technical questions, they neglect some of the meta-technical issues that cause injustice where data is involved. Drawing from lessons learned applying digital technologies to issues in the developing world, this talk will discuss the systematic challenges of applying data to social problems, pose difficult questions at the intersection of statistics and social justice, and make recommendations for data scientists who want to see analytics used for positive ends.
Bio: Kentaro Toyama is W. K. Kellogg Professor of Community Information at the University of Michigan School of Information, a fellow of the Dalai Lama Center for Ethics and Transformative Values at MIT, and author of Geek Heresy: Rescuing Social Change from the Cult of Technology. Previously, Kentaro taught mathematics at Ashesi University in Ghana, conducted research in computer vision and artificial intelligence at Microsoft, and co-founded Microsoft Research India, where he applied digital technologies to problems in international development.