#machine-learning-bias News & Analysis

2 articles tagged with #machine-learning-bias. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AINeutralarXiv – CS AI · Jun 27/10

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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.

AINeutralarXiv – CS AI · May 96/10

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Tuning Derivatives for Causal Fairness in Machine Learning

Researchers introduce a new mathematical framework for detecting and mitigating algorithmic bias in machine learning systems by using path-specific derivatives to distinguish between legitimate and illegitimate causal pathways. The approach extends fairness concepts to continuous protected attributes like age, addressing limitations in existing methods that primarily handle categorical variables.

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