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Those designing healthcare algorithms must become actively anti-racist · Study

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ArticleThose designing healthcare algorithms must become actively anti-racist

Kellie Owens2020

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Articlegreen2020

Those designing healthcare algorithms must become actively anti-racist

Kellie Owens · Alexis N. Walker

56 citations·0/0 files stored·Nature Medicine

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Ethics and Social Impacts of AI Artificial Intelligence in Healthcare and Education Race, Genetics, and Society

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References

2019Dissecting racial bias in an algorithm used to manage the health of populations 2012CRITICAL QUESTIONS FOR BIG DATA 2018Algorithms of Oppression 2016Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy 2019Review of “Race After Technology: Abolitionist Tools for the New Jim Code”2018Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor 2019A meta-analysis of procedures to change implicit measures.2019Assessing risk, automating racism

Cited by

2022Enabling Fairness in Healthcare Through Machine Learning 2022The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning 2021Reducing racial bias in AI models for clinical use requires a top-down intervention 2023Clinical algorithms, racism, and “fairness” in healthcare: A case of bounded justice 2022Algorithmic fairness in pandemic forecasting: lessons from COVID-19 2024

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Year2020

Typearticle

Published2020-09-01

VenueNature Medicine

ISSN-L1078-8956

Languageen

Citations56

Files0 public / 0

ShelfEthics and Social Impacts of AI

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doi10.1038/s41591-020-1020-3

openalex_workW3084028349

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