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. 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.

The rich are not exempt from this reality, but it’s the poor and working class who are most endangered by it. Predictive policing algorithms launder racial bias and reproduce inequality. Reputational scores based on historical data reinforce the lopsided structure of American society, further advantaging the already advantaged and marginalizing the marginalized.

Virginia Eubanks, associate professor of political science at the University at Albany, SUNY, has spent the past several years exploring how automation has played out in the American welfare system. Her new book, Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, investigates three experiments in which algorithms are replacing or augmenting human decision-making in public assistance: Indiana’s automated Medicaid eligibility process; Los Angeles’s coordinated entry system for the homeless; and Allegheny County, Pennsylvania’s predictive algorithm for assessing childhood risk of abuse and neglect.