Algorithmic Realism
“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
“Ben Green’s Algorithmic Realism is … credible, down to earth, sensible … much more concrete than either the Utopians or the Doomers.” —Andrew Keen
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Press Kit
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.
Reviews and Interviews
Tech Policy Press: A Data Science Playbook for Social Justice
The Data and AI Chief Podcast: Why AI for Good Fails and How Leaders Can Fix It
Keen On America: Super Unsexy By Design
Good Day Chicago: Who pays the price for AI and data center growth?
The Tech Humanist Show: When Data Centers Come to Town
Dans Les Algorithmes: For Algorithmic Realism: Change Data Science!
Table of Contents
Detailed Guide to the Algorithmic Realism Workflow ix
Acknowledgments xi
1. Data Science’s Dilemma 1
2. Foundations of Algorithmic Realism 33
3. Scoping Realist Data Science Projects 63
4. Executing Realist Data Science Projects 95
5. Challenging Inequality: From Fairness Metrics to Structural Interventions 125
6. Improving Human Decision-Making: From Explanations to Algorithm-in-the-Loop Systems 157
7. AI and the Future of Data Science 189
Notes 215
Bibliography 241
Index 261