Political Scientist and Author
Virginia Eubanks is an American political scientist and associate professor at the University at Albany, SUNY, who studies the intersection of technology and social justice. Her book Automating Inequality exposes how algorithmic decision making systems disproportionately harm economically disadvantaged communities.
by Virginia Eubanks
Virginia Eubanks investigates three case studies of automated decision systems targeting the poor: Indiana's automated welfare eligibility system, a coordinated entry system for homeless services in Los Angeles, and a predictive model for child abuse in Allegheny County, Pennsylvania. She reveals how these digital tools create a modern poorhouse that intensifies surveillance and punishment of vulnerable populations under a veneer of technological neutrality.
The books Virginia Eubanks references, cites, and recommends most frequently.
by Michelle Alexander
Alexander argues that mass incarceration has functioned as a racial caste system analogous to Jim Crow, using the War on Drugs to legally strip Black Americans of voting rights, employment, housing, and civic standing. She contends that color-blind rhetoric masks the racialized design and outcomes of modern criminal justice.
by Donella Meadows
Meadows explains how systems, from economies to ecosystems, behave through feedback loops, stocks, and flows. Most interventions fail because we address symptoms rather than the underlying structure driving the problem.
by Richard Thaler
Thaler and Sunstein argue that small changes in how choices are presented, nudges, can dramatically improve decisions without restricting freedom. Choice architecture is a powerful tool for public policy and beyond.
by Daniel Kahneman
Kahneman reveals that our minds run on two systems: fast intuition and slow deliberation. Most errors in judgement come from trusting System 1 when the situation demands System 2's careful analysis.
Who Virginia draws from, and who draws from Virginia — aggregated across every book in this collection. Counts show the number of citation links, not the depth of each one.