Risky and illegal behavior is requested to be considered, while thatīehavior is also actively criminalized by police. She critiques the invasive questioning for gathering predictive data and the maintaining of that data for likely surveillance. The majority of the book discusses specific examples of how digital tools fail the poor and put their health and livelihoods at risk.Ĭhapter 2 deals with the loss of healthcare as states roll out automated tools for processing claims.Ĭhapter 3 deals with LA's coordinated entry system for the homeless, which matches unhoused people with appropriate resources based on algorithmically ranking needs. Illuminates how automated tools are being quickly implemented with little to no political discussions about the repercussions. Through empirical work over the years with both poor recipients of state benefits and those working in courts and offices on these benefits, she This book specifically focuses on how digital tools surveil and police the resources the poor need to survive.
The data collected becomes a means of reinforcing marginality, which she refers to as "collective red-flagging, a feedback loop of injustice" (pg. She points out that marginalized groups - people of color, migrants, minority religious groups, sexual minorities, and the poor - are monitored more closely by practices of data collection, as they try to access public benefits, cross borders, get healthcare, and exist in highly policed neighborhoods. Martin's Publishing Group, 2018.Įubanks focuses on the predictive tools used to monitor groups of people, for everything from insurance fraud to loan approvals, and how those predictions can have profound effects, particularly on the mostĭisadvantaged. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the PoorĮubanks, Virginia. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. United States, St.
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