AINeutralarXiv – CS AI · 7h ago7/10
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Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinants
A research position paper argues that algorithmic fairness frameworks should move beyond focusing on sensitive attributes like race and gender to examine structural injustice through social determinants—contextual variables that shape outcomes systemically. The authors demonstrate through college admissions models, census data analysis, and healthcare screening applications that fairness interventions centered solely on sensitive attributes can paradoxically create new forms of structural injustice.