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Towards better Data Science to address racial bias and health equity

Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and c...

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Detalles Bibliográficos
Autores principales: Nsoesie, Elaine O, Galea, Sandro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896874/
https://www.ncbi.nlm.nih.gov/pubmed/36741434
http://dx.doi.org/10.1093/pnasnexus/pgac120
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author Nsoesie, Elaine O
Galea, Sandro
author_facet Nsoesie, Elaine O
Galea, Sandro
author_sort Nsoesie, Elaine O
collection PubMed
description Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and communication approaches to be antiracist and focused on serving minoritized groups that have long-standing worse health indicators than majority groups. In this perspective, we propose eight tenets that could shape a Data Science for Racial Health Equity research framework.
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spelling pubmed-98968742023-02-04 Towards better Data Science to address racial bias and health equity Nsoesie, Elaine O Galea, Sandro PNAS Nexus Perspective Data Science can be used to address racial health inequities. However, a wealth of scholarship has shown that there are many ethical challenges with using Data Science to address social problems. To develop a Data Science focused on racial health equity, we need the data, methods, application, and communication approaches to be antiracist and focused on serving minoritized groups that have long-standing worse health indicators than majority groups. In this perspective, we propose eight tenets that could shape a Data Science for Racial Health Equity research framework. Oxford University Press 2022-07-26 /pmc/articles/PMC9896874/ /pubmed/36741434 http://dx.doi.org/10.1093/pnasnexus/pgac120 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the National Academy of Sciences. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Perspective
Nsoesie, Elaine O
Galea, Sandro
Towards better Data Science to address racial bias and health equity
title Towards better Data Science to address racial bias and health equity
title_full Towards better Data Science to address racial bias and health equity
title_fullStr Towards better Data Science to address racial bias and health equity
title_full_unstemmed Towards better Data Science to address racial bias and health equity
title_short Towards better Data Science to address racial bias and health equity
title_sort towards better data science to address racial bias and health equity
topic Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9896874/
https://www.ncbi.nlm.nih.gov/pubmed/36741434
http://dx.doi.org/10.1093/pnasnexus/pgac120
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