Algorithmic Realism
Coming October 6 from MIT Press
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Algorithmic Realism is a remarkable book that deftly bridges cultures of computing and cultures of critique. In accessible language, Ben Green demonstrates how conventional data science has simultaneously been too ambitious and not ambitious enough when it comes to serving the public interest. He lays out a new workflow for data science grounded in pragmatism and oriented toward justice. This is essential reading for students, educators, professionals, and scholars alike.
—Catherine D’Ignazio, Associate Professor of Urban Science and Planning and Director of the Data + Feminism Lab, MIT; author of Counting Feminicide.
A bold new vision for data science—and a practical, step-by-step guide for how data scientists can contribute to social justice.
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.