Book

Race After Technology: Abolitionist Tools for the New Jim Code

📖 Overview

Race After Technology examines how racial discrimination and bias become embedded in modern technological systems and algorithms. Through analysis of facial recognition, predictive policing, and other technologies, Benjamin introduces the concept of the "New Jim Code" - automated systems that reinforce racial hierarchies under the guise of neutral progress. The book presents case studies of discriminatory design in healthcare, law enforcement, education and social services. Benjamin documents how seemingly objective technologies can amplify existing inequalities and create new forms of racial profiling and surveillance. Data, research, and real-world examples illustrate the impacts of biased technology on communities of color. The text incorporates perspectives from activists, researchers, and technology practitioners working to expose and address algorithmic discrimination. At its core, the book challenges the myth of technological neutrality and calls for critical examination of who benefits from and who is harmed by emerging technologies. It presents a framework for understanding how racism operates in the digital age and proposes pathways toward more equitable technological futures.

👀 Reviews

Readers appreciate Benjamin's clear examples of how technology and algorithms perpetuate racial inequality, from healthcare apps to criminal justice software. Many note the book provides a framework for understanding tech bias without requiring deep technical knowledge. Likes: - Connects historical racism to modern tech systems - Accessible writing style for non-technical readers - Practical suggestions for addressing algorithmic bias Dislikes: - Some find the academic language dense - Critics say solutions offered are too theoretical - Several readers wanted more concrete examples - Some note repetitive points across chapters "Explains complex concepts without oversimplifying," writes one Goodreads reviewer. "Too much theory, not enough practical fixes," notes an Amazon review. Ratings: Goodreads: 4.3/5 (2,100+ ratings) Amazon: 4.6/5 (450+ ratings) Google Books: 4/5 (200+ ratings) The book ranks among the top 100 bestsellers in Technology & Society on Amazon and earned recognition on multiple "Best Books About Tech Ethics" lists.

📚 Similar books

Algorithms of Oppression by Safiya Noble This investigation reveals how search engines perpetuate racial and gender biases through their algorithms and data structures.

Weapons of Math Destruction by Cathy O'Neil The book examines how mathematical models and algorithms in finance, education, and criminal justice create discriminatory systems that harm marginalized communities.

Automating Inequality by Virginia Eubanks Through case studies of automated systems in public services, this work demonstrates how digital decision-making tools discriminate against poor and working-class people.

Dark Matters: On the Surveillance of Blackness by Simone Browne This analysis traces the historical roots of surveillance from slavery to present-day technologies that disproportionately monitor Black communities.

The Age of Surveillance Capitalism by Shoshana Zuboff The book maps how tech companies extract and monetize personal data, creating new forms of power inequality and social control.

🤔 Interesting facts

📚 Author Ruha Benjamin coined the term "New Jim Code" to describe discriminatory designs embedded in modern technology, drawing a parallel to the historical Jim Crow laws. 🔍 The book examines how facial recognition systems often misidentify Black faces at higher rates than white faces, with some systems showing error rates up to 34% for darker-skinned women. 🎓 Benjamin teaches at Princeton University as a professor in African American Studies and is the founding director of the Ida B. Wells Just Data Lab. 💡 The book introduces the concept of "default discrimination," where seemingly neutral technological design choices can perpetuate existing social inequalities. 🌐 The work builds on earlier scholarship about algorithmic bias, including Joy Buolamwini's groundbreaking research at MIT Media Lab on racial and gender bias in AI systems.