Virginia Eubanks

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.… (plus d'informations)

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/… (plus d'informations)

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.… (plus d'informations)