The Deadly Consequences Of Unpredictable Code

As a child, mathematics was Cathy O’Neil’s passion: “math provided a neat refuge from the messiness of the real world.” After a stint in academia, she began working for a hedge fund (“the smuggest of the players on Wall Street”) just before the 2008 crash. That’s when she recognised the danger posed by mathematical models or, as she neatly terms them in this fascinating book, Weapons of Math Destruction. Her main point is that predictive models are never neutral but reflect the goals and ideology of those who create them. They also tend to load the dice against poor people, reinforcing inequality in society. From calculating university rankings or credit ratings and processing job applications to deciding what advertising you see online or what stories appear in your Facebook news feed, algorithms play an increasingly important role in our lives. Even the police use big data to help them predict where crimes may occur. The problem, as O’Neil so eloquently demonstrates, is that these algorithms are often incapable of reflecting the real world: “mathematical models should be our tools, not our masters.” Credit: P.D. Smith for The Guardian, 5 July 2017.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy is published by Penguin.

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