The whole book title is Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor.

This was a real eye opener for me. I grew up in a working class family, and we did have a time when we were quite poor, when my dad was on strike for about a year back in the 1970s, and we did not have much money. Also, my mom and dad divorced, and I was lucky to get scholarships to attend college with my mom being a single parent effectively. She did get income from my dad until I turned 18 I think.

Here is a good review of it. I first heard about the book from a talk about information privacy and AI and libraries by Alison Macrina of the https://libraryfreedom.org/ project. It was listed in the further reading section of her talk.

Anyway, the author, Virginia Eubanks, covers three case studies. The first is in Indiana, the second is in Los Angeles, and the third is in Pittsburgh. Chapter 5, The Digital Poorhouse, does a fine job of summing up the previous chapters. Essentially, the United States does a great job of using technology to track and police poor people. The system criminalizes poverty to keep poor people in that state. The system is designed to be opaque so that people can’t see how it really works.

Chapter 6 addresses ways to dismantle the digital poorhouse. I don’t see her recommendations coming to fruition any time soon. She brings up rhetoric from Martin Luther King, Jr., but technology has its claws so deep into law enforcement and in social support systems all across the country, I don’t think people will listen to the words of MLK, Jr. to dismantle those systems. She presents an Oath of Non-Harm for an Age of Big Data on pages 212-213. It will take a lot of work to convince companies to agree to that Oath. (While Google used to use the phrase — “Don’t be evil,” but that is now a former motto.)

I see it as a collective action problem. Even though she shows that the majority of people use social services as a temporary or full-time poor person, most people don’t see that Big Data has harmed them in any way. They might be convinced that Big Data has harmed some or many people, but it has not hurt them, yet. There needs to be more and better stories that get people to understand that Big Data hurting their neighbors is also hurting them. (People react better to stories than they do to plain old evidence and data, ironically.) That is when the voting public will act to change laws and policies when it comes to Big Data and the monitoring and policing of poor people in computer systems.

This book came out in 2017. It would be interesting to see how systems have changed with the Pandemic in the last three years.