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Social Policy

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Research Frontiers in Algorithmic Bias in Welfare Eligibility Systems

Investigates how machine learning algorithms perpetuate discrimination in automated benefit allocation and social program access decisions.

Proxy Discrimination in Automated Benefit Eligibility
Feedback Loops Between Welfare Algorithms and Poverty Stratification
Opacity-Driven Inequity in Social Security Classification
Intersectional Bias Amplification in Means-Testing Systems
Historical Data Contamination in Welfare Prediction Models
Algorithmic Gatekeeping and Access Disparities by Demographic
Compounding Errors in Multi-Stage Eligibility Determination
Legitimacy Gaps When Algorithms Replace Human Caseworkers
Protected Attribute Inference in Welfare Assessment Algorithms
Incentive Misalignment Between Efficiency and Equitable Distribution

All Social Policy PhD categories