Machine learning algorithms parse that data to assess our worthiness for public benefits, for jobs, for loans, for insurance, and for suspicion in the criminal justice system. Full of heart-wrenching and eye-opening stories, Automating Inequality is a deeply researched and passionate book that could not be timelier.We live in what legal scholar Frank Pasquale has called a “scored society.” Corporations and governments collect unprecedented amounts of data about us - our habits, our histories, our beliefs, our desires, our social networks. Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on economic inequality and democracy in America. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor. Today, automated systems control which neighborhoods get policed, which families attain resources, and who is investigated for fraud. Since the dawn of the digital age, decision-making in finance, employment, politics, health care, and human services has undergone revolutionary change.
In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect. The State of Indiana denied one million applications for health care, food stamps, and cash benefits in three years-because a new computer system interpreted any application mistake as “failure to cooperate.” In Los Angeles, an algorithm calculates the comparative vulnerability of tens of thousands of homeless people in order to prioritize them for an inadequate pool of housing resources. Everyone needs to understand that technology is no substitute for justice.” Its argument should be widely circulated, to poor people, social service workers and policymakers, but also throughout the professional classes.
“Riveting (an accomplishment for a book on technology and policy). How High-Tech Tools Profile, Police, and Punish the Poor
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