Ben Green
Assistant Professor at the University of Michigan School of Information
Ben Green is an assistant professor in the University of Michigan School of Information and an assistant professor (by courtesy) in the Gerald R. Ford School of Public Policy. He holds a PhD in Applied Mathematics from Harvard University, with a secondary field in Science, Technology, and Society. Ben studies the ethics of government algorithms, with a focus on algorithmic fairness, human-algorithm interactions, and AI regulation. Through his research, Ben aims to support design and governance practices that prevent algorithmic harms and advance social justice. His first book, The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future, was published in 2019 by MIT Press. He is working on a second book, Algorithmic Realism: Data Science Practices to Promote Social Justice. Ben is also a faculty associate at the Berkman Klein Center for Internet & Society at Harvard and a fellow at the Center for Democracy & Technology.
Algorithmic Realism: Data Science Practices to Promote Social Justice
Data scientists have confronted a gap between their desire to improve society and the harmful impacts of their creations. While algorithms once appeared to be valuable tools that advance social justice, today they appear to be dangerous tools that exacerbate inequality and oppression. In this talk, I argue that improving society with algorithms requires transforming data science from a formal methodology focused on mathematical models into a practical methodology for addressing real-world social problems. The current approach to data science—which I call “algorithmic formalism”—focuses on the formal mathematical attributes of algorithms. As a result, even when data scientists follow disciplinary standards of rigor, their attempts to improve society often entrench injustice. In response to these limitations, I introduce “algorithmic realism,” a data science methodology that designs and evaluates algorithms with a focus on real-world impacts. Algorithmic realism expands the data science pipeline, providing concrete strategies for how data scientists can promote social justice. This new methodology also suggests more socially beneficial directions for the burgeoning fields of data science ethics, algorithmic fairness, and human-centered data science.