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. ( )