Machine learning for environmental justice: Dissecting an algorithmic approach to predict drinking water quality in California

Author:

Karasaki Seigi,Morello-Frosch Rachel,Callaway Duncan

Publisher

Elsevier BV

Reference54 articles.

1. The color of algorithms: an analysis and proposed research agenda for deterring algorithmic redlining artificial intelligence and predictive algorithms: why big data can Lead to big problems;Allen;Fordham Urban Law J.,2019

2. The drinking water disparities framework: on the origins and persistence of inequities in exposure;Balazs;Am. J. Public Health,2014

3. Social disparities in nitrate-contaminated drinking water in California’s San Joaquin Valley;Balazs;Environ. Health Perspect.,2011

4. Environmental justice implications of arsenic contamination in California’s San Joaquin Valley: a cross-sectional, cluster-design examining exposure and compliance in community drinking water systems;Balazs;Environ. Health,2012

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