Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor

Eubanks’s partner Jason was randomly attacked one night and needed urgent medical care. Due to a mistake in the medical system, getting insurance coverage became difficult, and Eubanks realized that her family file had been “red-flagged” by the system (2). As she battled insurance companies, “each dreadful pink envelope” (4) that arrived reminded them of their debts.

From this personal anecdote, Eubanks moves on to a bigger example of faulty data: In 2004, Maine Republican governor Paul LePage demonized families receiving cash benefits from Temporary Assistance to Needy Families (TNAF). His polemic misinterpreted data, implying families were using welfare to purchase cigarettes and alcohol. As a result, the state instituted new rules for accessing TNAF, like banning families from accessing out-of-state ATMs—rules that were impossible to obey and vague enough to entrap people. One stereotype of poor people holds that they are trapped by new systems because of technological illiteracy. However, Eubanks points out that in her hometown of Troy, New York, the poor and working class women she knows are familiar with databases and technology.

The book will look at three case studies of social services and governance in the high-tech surveillance state: Indiana’s welfare system, the unhoused registry and coordinated entry system of LA, and the predictive model of abuse deployed in Allegheny County, Pennsylvania. Each story will detail a different destructive element of automated technology.

Eubanks’s research comes from over a hundred interviews with Americans from different geographical and racial backgrounds. Eubanks spoke most with working-class people of color, many of whom were already involved in housing activism and coordinated volunteer community groups. She also talked to “caseworkers, activists, policy-makers, program administrators, journalists, scholars, and police officers” (11), and found at the results of her research that poor people face dire outcomes at the hands of digital poverty management: “Automated decision-making shatters the social safety net, criminalizes the poor, intensifies discrimination, and compromises our deepest national values” (12). These systems have resurrected in digital form the poorhouse, which is rooted in American history and accepted as normal and moral in present day society.