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.