Overview

An expansive vision for data equality that goes beyond algorithmic fairness. When we gave algorithms power over our world, we hoped that the apparent neutrality of machine thinking would create a more egalitarian age. Yet we are more divided than ever, staring down threats to democracy itself. In Data Equals, Colin Koopman argues that data technologies fail us so often because we built them around a deficient notion of equality. It is not enough, Koopman explains, that algorithms engage everyone's data with the same measuring stick. The data themselves are all too often structured in ways that obscure and exacerbate stratifying distinctions. Koopman contends that we must also work to ensure that those people subject to computational assessment enter data systems on equal terms. Part philosophical argument, part practical guide (replete with case studies from education technology), Data Equals offers novel methods for realizing democratic equality in a digital age.

Full Product Details

Author:   Colin Koopman
Publisher:   The University of Chicago Press
Imprint:   University of Chicago Press
Dimensions:   Width: 15.20cm , Height: 2.50cm , Length: 22.90cm
Weight:   0.653kg
ISBN:  

9780226842240

ISBN 10:   022684224
Pages:   296
Publication Date:   16 September 2025
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Introduction: Reconstructing Democratic Equality in Data Technology from Paper Records to Artificial Intelligence Part 1: Data Equality 1. Data Hierarchy, Technological Neutrality, and Algorithmic Fairness: Some Obstacles 2. Data Equality in Social Structure: An Opening Part 2: Equality 3. Structural Equality: A Pragmatist Account of Democratic Equality 4. A Conceptual Anatomy of Equal Treatment: Equitable Entry + Fair Processing Part 3: Data 5. Structural Data: Formats + Algorithms 6. Format Anatomies: A Methodology for Dissecting Data Part 4: Democratic Equality in Education Data 7. Artificial Intelligence for Personalized Learning: An Anatomy of Learner-Model Formats 8. Collaboration versus Personalization in Democratic Education: Evaluating Equality in Learner Data Conclusion: Becoming Data Equals Acknowledgments Notes Bibliography Index

Reviews

“Data Equals is an urgent, timely, and ambitious call to reconstruct the modern data order, which now suffocates democracies with increasingly powerful technologies of separation that draw our lives further and further apart and leave us unable to encounter one another as equals. With clarity and purpose, Koopman revitalizes the philosophical tradition of pragmatism to remind us of the possibilities for egalitarian politics and how data might finally serve them.” -- Shannon Vallor, University of Edinburgh “Data Equals is a compelling, original, and important contribution to the literature on data studies, pragmatism, political philosophy, and philosophy of technology. It brings together perspectives that are rarely examined in concert, providing a refreshing take on data policy and the ethics of AI that challenges the idealized approach that characterizes much of the contemporary literature. Data Equals is a must-read for anyone interested in issues concerning equality and technology.” -- Carlos Montemayor, San Francisco State University

Author Information

Colin Koopman is professor of philosophy and director of new media and culture at the University of Oregon. His books include How We Became Our Data: A Genealogy of the Informational Person, also published by the University of Chicago Press.

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