Speaker: Ben Green

Institution: University of Michigan

Date: 14:00 GMT, 29th October, 2026

Talk Title: Algorithmic Realism: Data Science Practices to Promote Social Justice

Abstract: The field of data science faces a moral crisis. Despite the desires of data scientists to develop algorithms for good, algorithms regularly produce injustice in practice. Given these persistent harms, the field must reflect on difficult questions about its identity and future. Can data science be a force for promoting social justice in the world? What practices should data scientists follow to achieve this goal?

In Algorithmic Realism, Ben Green presents a bold and interdisciplinary approach to data science. Drawing on his experience practicing data science in the public interest, he argues that improving society with algorithms requires transforming data science from a formalist methodology focused on mathematical models into a practical methodology focused on addressing real-world problems. By providing an expanded framework for the “data science workflow”—the steps that characterize the algorithm-development process—he offers a practical, step-by-step guide describing how data scientists can apply their skills in service of social justice. Through these contributions, the book reveals a vision for a renewed, but realistic, optimism about data science’s potential to foster a more equitable world.

Bio: Ben Green is an Assistant Professor of Information and Public Policy at the University of Michigan and a Faculty Associate at the Berkman Klein Center for Internet & Society at Harvard. Through his research, writing, and advocacy, Ben contributes practical lessons for achieving a world in which AI improves society rather than exacerbating inequality and oppression. He has published op-eds in outlets including The New York Times, The Wall Street Journal, and The Hill, and has been quoted in newspapers such as The New York Times, The Washington Post, and the Los Angeles Times. He is the author of two books from the MIT Press: Algorithmic Realism: Data Science Practices to Promote Social Justice (2026) and The Smart Enough City: Putting Technology in Its Place to Reclaim Our Urban Future (2019).