Automating Inequality - bookrastinating.com

272 pages

Published July 10, 2018 by St. Martin's Press.

ISBN:: 978-1-250-07431-7

A powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity

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 …

A powerful investigative look at data-based discrimination—and how technology affects civil and human rights and economic equity

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 lays 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.

1 edition

Garrett reviewed Automating Inequality by Virginia Eubanks

Review of 'Automating Inequality' on 'Goodreads'

4 stars

One of the greatest sins of tech workers is to imagine that technology can solve everything, that there's some sort of algorithm that can just handle all the work in the world while we actively imbue our biases into them to create what the author here calls a "digital poorhouse". The concept is sharp, the examples are detailed, authoritative and include the human side, something that academics often shy away from. This book challenges your expectations of tech by showing examples that are brazenly attempting to limit welfare recipients through bureaucracy rather than improving their lives or treating the homeless as some form of over-surveilled livestock that isn't granted the same rights as housed people. It's a powerful walk through some of the most unfortunate things we've tried to push despite accusations of immorality, whether the underlying cause is cruelty, lack of resources, or simply apathy towards the poor.

SocProf reviewed Automating Inequality by Virginia Eubanks

Review of 'Automating Inequality' on 'Goodreads'

4 stars

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. …

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.