The New Jim Code: Race, Carceral Technoscience, and Liberatory Imagination in Everyday Life

The New Jim Code examines how racism becomes embedded in technology through algorithms, artificial intelligence, and data systems. Muhammad draws connections between historical forms of racial oppression and modern technological tools that perpetuate bias and discrimination. Through case studies and research, the book explores automated decision-making systems in policing, housing, education, and employment. The analysis reveals how these technologies can reinforce existing racial hierarchies while appearing neutral and objective on the surface. Muhammad documents the experiences of communities affected by surveillance and predictive technologies, alongside the efforts of activists and organizations working to expose and challenge these systems. The narrative interweaves historical context with contemporary examples to trace patterns of technological control. This work contributes to critical discussions about power, race, and technology in American society, offering frameworks for understanding how innovation can either advance or obstruct racial justice. The book raises fundamental questions about the relationship between technological progress and social equity.

There are not enough internet reviews to create a summary of this book. Instead, here is a summary of reviews of Khalil Gibran Muhammad's overall work: Readers praise Muhammad's detailed research and clear explanation of how crime statistics were historically weaponized against Black Americans. Many note how "The Condemnation of Blackness" helps them understand current racial dynamics in policing and criminal justice. What readers liked: - Thorough documentation and primary sources - Clear connections between historical patterns and present-day issues - Accessible academic writing style that engages non-scholars - Balanced analysis that acknowledges complexity Common criticisms: - Dense academic language in some sections - Repetitive points in later chapters - Some readers wanted more discussion of solutions Ratings across platforms: Goodreads: 4.5/5 (2,000+ ratings) Amazon: 4.7/5 (500+ ratings) Sample reader comment: "Muhammad methodically traces how statistics were manipulated to create false narratives about Black criminality - essential historical context for understanding modern policing." - Goodreads reviewer Another notes: "Dense but rewarding read that changed how I view crime statistics and racial bias." - Amazon reviewer

Race After Technology by Ruha Benjamin Examines how technology perpetuates racial discrimination through algorithms and digital architectures.

Algorithms of Oppression by Safiya Noble Documents how search engines and data systems reinforce social inequalities and discriminate against people of color.

Weapons of Math Destruction by Cathy O'Neil Reveals how mathematical models and algorithms create invisible discrimination systems that impact employment, education, and criminal justice.

Dark Matters: On the Surveillance of Blackness by Simone Browne Traces the historical connections between surveillance practices and the control of Black bodies from slavery to present-day technologies.

Automating Inequality by Virginia Eubanks Investigates how digital decision-making systems in public services target and harm poor communities and communities of color.

📚 The book draws its title from Michelle Alexander's influential work "The New Jim Crow," extending the analysis to show how racial discrimination is embedded in modern technology and algorithms. 🔍 Khalil Gibran Muhammad is the great-grandson of Elijah Muhammad, who led the Nation of Islam from 1934 to 1975. 💻 The author examines how seemingly "neutral" technological systems, from facial recognition to predictive policing, often reproduce and amplify existing racial biases. 🎓 Muhammad is a professor at Harvard Kennedy School and the Radcliffe Institute, where he teaches about racial justice, policing, and criminal justice reform. 📊 The book explores real-world examples of algorithmic bias, including a case where Amazon's facial recognition software falsely matched 28 members of Congress with criminal mugshots, disproportionately misidentifying people of color.