Automating Inequality

Name: Automating Inequality
Author: Virginia Eubanks

Virginia Eubanks

Virginia Eubanks systematically investigates the impacts of data mining, policy algorithms, and predictive risk models on poor and working-class people in America. She argues that these tools create a 'digital poorhouse' that profiles, polices, and punishes the poor, operating more quickly and at greater scale than the physical poorhouses of previous generations.

5 score

#561 overall · #18 in Data Science

↓6

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Data Science(22)Machine Learning(41)Data(40)AI / ML(51) Intermediate AlgorithmsSoft Skills

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🟢 Developer Verdict

Examines how data mining, policy algorithms, and predictive models create a 'digital poorhouse' that profiles and punishes the poor.

Read this if

✓ You want to understand the social impact of data science.
✓ You are concerned about algorithmic bias and inequality.
✓ You seek to grasp the ethical implications of tech on vulnerable groups.

Skip this for now if

✗ You are looking for hands-on technical guidance or code examples.
✗ You expect a deep dive into machine learning model architectures.
✗ You prefer content focused on practical solutions rather than systemic issues.