This 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. ( )

Content Note: child abuse/neglect

“Plot”:
Looking at different algorithms and automated systems that are supposed to help manage poverty and its side-effects, Eubanks traces those apparently new inventions back to their historic roots and shows how these seemingly objective tools contribute to discrimination of the poor.

Automating Inequality draws on many examples to outline how the way the USA deals with poverty has developed over time, and how those historical roots are still present. Technology, far from being a neutral, helpful tool can be seen to continue and even deepen injustices, even where tempered by human decision making. It’s a good read that makes many good points.

Read more on my blog: https://kalafudra.com/2022/09/01/automating-inequality-virginia-eubanks/ ( )

Here 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. ( )

The 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. ( )

Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks is a report on the use of technology in determining government assistance programs. Eubanks is the co-founder of Our Knowledge, Our Power (OKOP), a grassroots anti-poverty and welfare rights organization, and is Associate Professor in the Department of Women's Studies at the University at Albany, SUNY.

Public assistance programs are seen as a drag on the economy to many people. People work hard for their money don't want to see their tax dollars abused. Reagan exaggerated stories of welfare queens. The 1970s were filled with images of Caddilac's parked in front of welfare offices. Public assistance is typically seen as an abused system. The good that it does is under-reported when compared to the abuse.

Over seventy percent of full-time workers say they live paycheck to paycheck. The average American also has nearly $16,000 in credit card debt. For those seeking an education, student loan debt piles up faster than job opportunities. Many Americans are balancing on the edge of homelessness and bankruptcy.

Eubanks looks at three separate areas in three different parts of the country and examines what automation has done in determining benefits and the problems it causes. Poverty in America is real and a growing problem. We see it every day and do our best to block it out. Americans also have a history of moving away from poverty -- out of the cities and into the suburbs and back again.

The first area Eubanks describes is automation and privatization of public services to save money and limit fraud (which is very small). Applications are done over the phone to a call center (which was problematic for the deaf) or online. In poor areas, libraries and librarians are overrun trying to provide internet service to patrons filing for benefits. In one case (years ago, I imagine) a woman added the food stamps phone number to her family and friends list because she spent so much time on the phone with them over benefits. When Indiana automated it was a disaster. Call centers and document centers did not follow through on paperwork many lost benefits for failure to cooperate when paperwork was lost. This was life-threatening to many on medication. Fixing problems was met with resistance, paperwork, and delays.

Skid Row in Los Angeles became the defacto homeless area. Keeping a defined area homelessness helped insulate the public from the homeless. Gentrification, however, pushed the homeless out of their "home." Arrests for sitting or laying on the sidewalk, confiscation of property, and basically criminalizing homelessness became the government's solution. In Pennsylvania, Child Services uses an algorithm to predict future behavior. Vendetta calls remain in the parent's/child's records. In both cases, algorithms have taken over for human interaction and understanding. Computers take certain answers but most of the time no matter what is being filled out "Other" is filled out especially when something as important as physical and mental health. Computers are poor interpreters of "other."

Automating Inequality demonstrates the problems of algorithms and automation and what it does identify the poor and many cases work to keep the poor poor. The system was intended to provide assistance for the short term and help people out of poverty has become a system to perpetuate poverty. An interesting report based on real-life examples and real-life workers. ( )

https://www.theguardian.com/technology/2019/oct/14/automating-poverty-algorithms...

See also: Weapons of Math Destruction by Cathy O'Neil

Happy day after labor day. If you’re sitting at a desk in your office, there’s a chance it was made by someone making two dollars an hour or less. Much less.

Of course, maybe you don’t have an office because you’re part of the majority of academic labor that is hired by the course or has a long-term non-permanent gig. But if you stop by, say, the Minnesota History Center or a major public university library to do some research, there’s a good chance you’re sitting at a table made by someone who’s incarcerated and paid far less than minimum wage.

If you watch California fires raging and feel for the heroic men and women on the front lines, carrying heavy equipment and wearing hot gear in 100-plus degree weather, bear in mind many of them are paid a dollar a day. They are incarcerated “volunteers.” This is part of what a national prison strike is about. You might have heard about it, but possibly not; it hasn't gotten much coverage.

You could argue that prison industries are a good thing because incarcerated people learn skills and work habits. It’s too bad that when they’re released they often can’t get a job because they have a criminal record. They probably don’t even get a chance to discuss with a human being what they learned while working in prison because a computer system kicked their application to the bottom using an algorithm. Chances are another algorithm will send them back to prison if they fail to adhere to parole requirements, like failing to have a job or permanent address or ability to pay for their parole expenses.

As it happens, I just finished reading Automating Inequality How High-Tech Tools Profile, Police, and Punish the Poor by Virginia Eubanks. I was especially curious about the algorithms used in social services, but it’s not just about technology, it’s a brilliant book about how we penalize poverty. It would make a great pairing with Evicted: Poverty and Profit in the American City by Matthew Desmond. Both books make it clear how badly shredded our social network is and how well hidden poverty is now that it’s treated as a personal failing rather than as a social issue we should solve because it’s bad for all of us, unnecessary in the richest nation on earth, and unjust.

Eubanks focuses her research in three locations: a failed approach to automate public benefits eligibility determination in Indiana that nevertheless managed to permanently remove many people eligible for benefits from getting help; an app for deciding who among the thousands of homeless in Los Angeles could get processed into housing; and a flawed system in Pennsylvania for deciding which children are at risk and should be removed from their homes. These attempts (with the exception of the first, which appears primarily a mean-spirited system for punishing the poor) are generally well-meant, but tackle the wrong problems. There’s a housing crisis in Los Angeles and across the country, and deciding who to shuffle into a limited number of shelter beds or subsidized housing doesn’t do anything to address that crisis. The algorithm implemented in Pennsylvania does a pretty good job of measuring the effects of poverty, but nothing to help children subject to abuse or neglect in households that aren’t already implicated in state systems because they receive benefits from Temporary Assistance for Needy Families or the Supplemental Nutritional Assistance Program. Wealthier families that experience problems that lead to child neglect don’t have public data gathered on them that might predict whether a child should be removed from the household. An employee of the company building this predictive system told Eubanks it would great to include data from babysitters, therapists, or rehab centers, but nobody on earth would agree to have such personal information gathered by the government. The poor have no choice but to be monitored, measured, and sorted, and now we do it with computers.

Eubanks frames this system in our history – first the poorhouse, then scientific charity, now a digital poorhouse that isolates and punishes using supposedly “unbiased” systems that are trained with biased data.

"Like the brick-and-mortar poorhouse, the digital poorhouse diverts the poor from public resources. Like scientific charity, it investigates, classifies, and criminalizes. Like the tools birthed during the backlash against welfare rights, it uses integrated databases to target, track, and punish . . .

"However, there are ways that the analogy between high-tech tools in public services and the brick-and-mortar poorhouse falls short. Just as the county poorhouse was suited to the Industrial Revolution, and scientific charity was appropriate for the Progressive Era, the digital poorhouse is adapted to the peculiar circumstances of our time. The country poorhouse responded to middle-class fears about growing industrial unemployment: it keeps discarded workers out of sight but nearby, in case their labor was needed. Scientific charity responded to native elites’ fear of immigrants, African Americans, and poor whites by creating a hierarchy of worth that controlled access to both resources and social inclusion.

"Today, the digital poorhouse responds to what Barbara Ehrenreich has described as a ‘fear of falling’ in the professional middle class . . . The digital poorhouse is born of, and perfectly attuned to this political moment. (pp 178, 183-4)

It’s nearly invisible, scalable, persistent, isolating, and will, in time, embrace all of us as webs of sticky personal data are woven around our lives.

Because of Labor Day, the Washington Post profiled a photographer whose works illuminated the hard lives of working children and made people care. I chose one of those photos for my personal website, not knowing much about it but because it’s beautiful and poignant – a girl turned away from a giant mechanical loom to gaze out a window. Lewis Hine had to fake his way into factories and fields to document child labor in photos like this one, and the human stories those photos told helped launch a movement to end it.

It’s hard to get inside and take pictures of the trade-secret digital poorhouse, but Automating Inequality does a great job of showing why we need to pay attention and unite in fighting economic and political systems that keep too many of us either in or just one medical emergency or job loss from isolating, humiliating, quantified poverty.

Americans 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. ( )

I liked this book, I thought it was well researched.

As the examples went on, I felt that Eubanks was padding the pages (although this is well deserved from the amount of work she put into it). Her examples of the omnipresent 'poor-house' and how we are reframing political/moral dilmemmas about our obligation to help (the poor) and how to think about inequality as an engineering problem of efficiency is so wrong and about how justice requires bending the rules (inhuman computers can not help but give inhumane justice) was very powerful to me. ( )

very 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. ( )

Target, 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 ( )