Against Prediction

Against Prediction examines the rise of actuarial methods and predictive tools in criminal justice systems. The book traces how statistical and probabilistic approaches have become embedded in law enforcement, sentencing, and parole decisions. Harcourt challenges the fundamental assumptions behind predictive policing and recidivism forecasting through empirical analysis and historical investigation. He presents evidence about the effectiveness of prediction tools and questions whether they actually reduce crime. The work analyzes key technical concepts like selective incapacitation and profiling while exploring their real-world implementation across various jurisdictions. Harcourt draws on case studies and data to demonstrate the concrete impacts of prediction-based practices. This scholarly critique raises essential questions about fairness, racial bias, and the proper role of statistics in criminal justice decision-making. The book contributes to broader debates about surveillance, discrimination, and the balance between security and civil liberties.

Readers note this academic text offers a critique of predictive policing and actuarial methods in criminal justice. Reviews point to Harcourt's rigorous analysis of data and historical examples. Readers appreciate: - Clear arguments against prediction tools in law enforcement - Historical context and real-world examples - Technical analysis supported by statistics - Challenge to common assumptions about profiling Common criticisms: - Dense academic writing style - Repetitive points throughout chapters - Limited discussion of alternative approaches - Some sections heavy on mathematical formulas Ratings: Goodreads: 3.9/5 (27 ratings) Amazon: 4.2/5 (8 ratings) One reviewer on Goodreads writes: "Important critique but could have been more concise." An Amazon reviewer notes: "Makes a compelling case against predictive policing, though the statistical sections may lose some readers." The book appears most popular among academic readers and those specifically interested in criminal justice policy.

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🔎 Bernard Harcourt developed his arguments against predictive policing while serving as a death row attorney, where he witnessed firsthand how statistical profiling affected criminal justice outcomes 📚 The book challenges three major applications of predictive methods: IRS audits, flight passenger screening, and criminal profiling - arguing that all three ultimately become counterproductive ⚖️ One of the book's key revelations is that even perfectly accurate prediction tools can paradoxically increase overall crime rates by concentrating police resources too narrowly 🎓 Harcourt teaches at Columbia University and École des Hautes Études en Sciences Sociales in Paris, bringing both American and European perspectives to his analysis of prediction in law enforcement 📊 The mathematical models criticized in the book share similarities with "actuarial methods" first developed by insurance companies in the 1800s to assess risk - methods that later spread to criminal justice