Audiobook7 hours
Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Written by Virginia Eubanks
Narrated by Teri Schnaubelt
Rating: 4 out of 5 stars
4/5
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About this audiobook
The State of Indiana denies one million applications for healthcare, foodstamps, and cash benefits in three years-because a new computer system interprets any 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. In Pittsburgh, a child welfare agency uses a statistical model to try to predict which children might be future victims of abuse or neglect.
Since the dawn of the digital age, decision-making in finance, employment, politics, health, and human services has undergone revolutionary change. Today, automated systems-rather than humans-control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.
In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lies dying, to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.
The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.
This deeply researched and passionate book could not be more timely.
Since the dawn of the digital age, decision-making in finance, employment, politics, health, and human services has undergone revolutionary change. Today, automated systems-rather than humans-control which neighborhoods get policed, which families attain needed resources, and who is investigated for fraud. While we all live under this new regime of data, the most invasive and punitive systems are aimed at the poor.
In Automating Inequality, Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. The book is full of heart-wrenching and eye-opening stories, from a woman in Indiana whose benefits are literally cut off as she lies dying, to a family in Pennsylvania in daily fear of losing their daughter because they fit a certain statistical profile.
The U.S. has always used its most cutting-edge science and technology to contain, investigate, discipline and punish the destitute. Like the county poorhouse and scientific charity before them, digital tracking and automated decision-making hide poverty from the middle-class public and give the nation the ethical distance it needs to make inhumane choices: which families get food and which starve, who has housing and who remains homeless, and which families are broken up by the state. In the process, they weaken democracy and betray our most cherished national values.
This deeply researched and passionate book could not be more timely.
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Reviews for Automating Inequality
Rating: 4.065573688524591 out of 5 stars
4/5
61 ratings6 reviews
- Rating: 3 out of 5 stars3/5This book is good and provides insight into how we profile and punish the poor. The most fascinating chapter, which I will read again, is the first one, on poor houses. There is a small fallacy in the book that lies in the subtitle. High-Tech Tools do not police and punish the poor. If you read the first chapter carefully, you will realize that we stigmatize, profile, and punish the poor. We often do not treat them as humans. The High-Tech tools we have at our disposal now have only made this more unfair and efficient. She does not explore the human tendency to stigmatize the poor. The book has another weakness. While she included many case studies in the book, Virginia did not sufficiently explore the technology angle.
- Rating: 4 out of 5 stars4/5Here is another sociological contribution to critical studies of the digital age. In this book, Eubanks uses three case studies of reconfiguration of social services to digital automation - what she dubs the "digital poorhouse" and their consequences. So, this book takes its place with Cathy O'Neill's Weapons of Math Destruction and David Lyon's framework of the surveillance society. In all three cases, digital systems serve as diversion (pushing people off social services), managing scarcity and criminalizing the poor, and using predictive analytics (based in invalid data and modeling) assess people based on potential future behavior. In addition, under the guise of technological objectivity - after all, algorithms are held to have no bias - socio-economic and political issues are reduced to technical problems to be solved with more intrusive data. But there is no salvation by software for socio-economic problems such as racism, poverty, or lack of affordable housing. This is the now-familiar trope of disruption that whose deployment on the poor no other social class would tolerate.
In other words, this book is as much about social problems and policies as it is about technology. The last chapter on what is to be done is not the best of the book (hence my 4-star), but the case studies and more general framework are well worth anyone's time.
This book also contains a warning: the use of data tools will not stay confined to the poor. Sooner or later, we will all be subjected to the "disruption" brought forth by data-driven management, from the public or the private sectors. - Rating: 5 out of 5 stars5/5The content is difficult, but so important. I don't know how people who are doing research like this don't end up either terribly angry or terribly defeated about what we're doing to our fellow humans.
- Rating: 4 out of 5 stars4/5Americans have a problem with poverty which we have converted into a problem with poor people. Thus, policymakers tout technology as a way to make various social programs more efficient, but they end up encoding the social problems they were designed to solve, thus entrenching poverty and overpolicing of the poor. Eubanks runs through three examples—welfare reform software in Indiana, homelessness service unification through comprehensive data collection and sharing in Los Angeles, and child abuse prediction in Pennsylvania—and shows that while they vary in how screwed up they are (Indiana terribly, Los Angeles a bit, and Pennsylvania very hard to tell), they all rely on assumptions that leave poor people more exposed to coercive state control that is both a result of and a contributor to the assumption that their problems are their own fault. It’s a distressing work, mainly because I have little faith that the problems Eubanks so persuasively identifies can be corrected.Here’s the overview: “Across the country, poor and working-class people are targeted by new tools of digital poverty management and face life-threatening consequences as a result. Automated eligibility systems discourage them from claiming public resources that they need to survive and thrive. Complex integrated databases collect their most personal information, with few safeguards for privacy or data security, while offering almost nothing in return. Predictive models and algorithms tag them as risky investments and problematic parents. Vast complexes of social service, law enforcement, and neighborhood surveillance make their every move visible and offer up their behavior for government, commercial, and public scrutiny.” Her recommendation, even as more punitive measures like work requirements that will require increased surveillance are being enacted, is for more resources and fewer requirements: homelessness isn’t a data problem, it’s a carpentry problem, and a universal basic income/health insurance would do a lot better than a gauntlet of forms in allocating care.
- Rating: 5 out of 5 stars5/5very insightful and well researched book. Very much focussed on the situation in the US which does not have a welfare state and so the specific examples that she describes are relatively difficult to map on developments in continental Europe. Still the underlying mechanisms and ideas are just as relevant for understanding current developments in places that have relatively well developed welfare systems.
- Rating: 4 out of 5 stars4/5Target, track, punish. Repeat.Notwithstanding what the French wrote on the Statue of Liberty, America hates its poor. It will spend billions to deny them help. In Automated Inequality, Virginia Eubanks says we manage the poor so we don’t have to eradicate poverty. Instead, we have developed a Digital Poorhouse – high tech containment of the poor and recording of their every action, association and activity. The great innovation today is the prediction model, using the child, the parents, neighbors and even the neighborhood to predict when a child should be removed and given to foster care – before anything has happened.America’s war against its poor goes back some 200 years. It has put them in poorhouses and debtors’ prisons, made it difficult or impossible for them to live freely and raise a family, denied them benefits set out in law, sterilized them, and contemplated encouraging them to just die. The latest iteration is high tech. Government tracks the movements, purchases, and habits of those unfortunate enough to seek its help. It’s all automated. Decisions are made by algorithms, and undoing the ensuing mess is somewhere between exasperating and impossible. Eubanks explores three very different and widely separated approaches to managing, manipulating and controlling the poor in Indiana, the homeless in Los Angeles, and the child welfare in Pittsburgh.-During the financial crisis, when millions lost jobs and homes, Indiana actually reduced the percentage of the legally poor on welfare from 38% to 8. It hired IBM to centralize all activity, including document collection. Local caseworkers disappeared, becoming call center agents. They were measured on productivity – how little time they spent with applicants. The slightest error in the 30 document process meant instant automatic denial of benefits. Applicants received a notice of “Failure to co-operate” with no explanation whatsoever. This could include failure to answer the phone for an interview the system rescheduled without notice, failure sign in the numerous places required, and failure of the system to scan and enter the documents submitted. One woman was confined to a hospital bed when they called her home. She was immediately cut off from all benefits, including Medicaid for her cancer, free transport to medical appointments, and foods stamps. They day after she died, she won her appeal.-Los Angeles has worked hard to gentrify Skid Row. Rather than allow renovation, it has actually removed more housing than there are homeless there. Rumors of the availability of a room can cause lineups for days. LA has spent $11 million collecting data on individuals, but almost all are still homeless. It has been an exercise in tracking and surveillance, with ever more intrusive questionnaires and interviews, mental health tests, and essentially no hope of permanent placement. But everyone goes through the process, often several times, providing intimate details to be used against them. Police apply huge pressure to get the poor out of there, adding to their life records. In 2006, they made 9000 arrests and 12,000 citations in an area with a population of maybe 15,000.-Allegheny County (Pittsburgh) has a data warehouse of every contact anyone has ever had with public services, including data like the date, amount and location of every purchase with a welfare card. It’s an average of 800 pieces of information per person. Algorithms decide if children are at risk of abuse or neglect. The error rate for both false positives and false negatives is high, putting children, and their parents, at risk. Naturally, an outsized percentage of cases involve those who are poor and black. But the reality is that most of the children investigated are not physically or sexually abused. They are poor.Eubanks rails against us looking the other way, being indifferent, fearing for our own status, and other such liberal guilt. It makes the book end badly. It detracts from the premise that big data is taking over entire lives, keeping people in their place and preventing the help lawmakers prescribe. The blame needs to stay at the top, even if the solution might come from the bottom. The punchline in all these scenarios is that poverty costs more money than it’s worth. President Nixon saw this in the late 60s. He proposed a national basic income. With money in bank accounts, the need for monitoring, surveillance, recordkeeping, data centers, courts and enforcement all but disappears. The system both pays for itself and improves lives. But America is at war with its poor, so “our vast and expensive public service bureaucracy primarily functions to investigate whether individuals’ suffering might be their own fault.”David Wineberg