Automating Inequality – How High-Tech Tools Profile, Police, and Punish the Poor is a non-fiction book by Virginia Eubanks.
Finished on: 26.8.2022

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.

Automating Inequality tackles a similar topic like Weapons of Math Destruction, so it invites comparison a little. And in direct comparison, I prefered WMD to Automating Inequality for the simple reason that it felt more systematic to me. Automating Inequality features a lot more case studies and anecdotes, showing different aspects of how technology and algorithms affect poor people and thus also BIPoC disproportionately. But it isn’t until the very end that it gave us a way to fit it all together. That was a little late for my taste.

But other than that, I found Automating Inequality to be very insightful. The history of poverty management in the USA and how it directly feeds into the design of technological tools today was really interesting. And even though Austrian/European history is very different in general, it’s easy to see the similarities as well. The way benefits are portrayed as something you have to earn, yet something shameful if you have to use it is definitely I know from the world around me.

Eubanks doesn’t just look at benefits allocation, like who gets health insurance or housing, but also at child abuse and neglect and attempts to predict at risk families. She makes very clear that child abuse is not limited to poor people, but that poverty ist often almost indistinguishable from neglect: insufficient clothing, heating or housing are considered neglect, and of course, they’re also part of what it means to be poor. With a system that seems less interested in supporting and more interested in punishing, poverty becomes doubly injurious.

Of course, those dynamics aren’t new, they didn’t start with technology or automated decision making. But neither is technology a way out. Rather it is a more efficient way to practice bias. Humans may be prejudiced, but their also empathetic. Technology is programmed in a way that leaves only the bias behind and none of the empathy.

To not replicate that mistake, Eubanks gives ample space to affected people to state their case and to show the human cost of a policy that values efficiency and cost saving over the people it is supposedly for. It’s infuriating and makes one even more wary of Big Data and everything that comes with it.

Summarizing: insightful.