Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor
Summary
Three case studies of automated decision-making in US public services: Indiana’s automated welfare eligibility system, Los Angeles’s coordinated entry system for unhoused people, and the Allegheny County (Pittsburgh) family screening algorithm for child welfare. Eubanks argues that these tools build a “digital poorhouse” that continues a long history of surveilling, sorting, and punishing poor people.
Why it’s useful here
- AI harms at the local, municipal scale: the scale where community governance can act. Counties and cities make these procurement decisions.
- Concrete cases for governance posts: who decided to adopt the system, who could appeal, and what an IAD analysis of these rules would show.
- A grounded, domestic version of Griziotti’s “whom to surveil, exclude” argument.
Key ideas
- The digital poorhouse
- Automated eligibility, ranking, and risk prediction in public services
- Automation hides political choices behind technical neutrality
Related
- mcquillan-2022-resisting-ai.md
- graeber-2015-utopia-of-rules.md