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Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study

OBJECTIVE: To better understand diverse experts’ views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data. DESIG...

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Autores principales: Nichol, Ariadne A, Bendavid, Eran, Mutenherwa, Farirai, Patel, Chirag, Cho, Mildred K
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320245/
https://www.ncbi.nlm.nih.gov/pubmed/34321310
http://dx.doi.org/10.1136/bmjopen-2021-052287
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author Nichol, Ariadne A
Bendavid, Eran
Mutenherwa, Farirai
Patel, Chirag
Cho, Mildred K
author_facet Nichol, Ariadne A
Bendavid, Eran
Mutenherwa, Farirai
Patel, Chirag
Cho, Mildred K
author_sort Nichol, Ariadne A
collection PubMed
description OBJECTIVE: To better understand diverse experts’ views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data. DESIGN: Three rounds of semi-structured surveys in an online expert panel using a modified Delphi approach. PARTICIPANTS: Experts in informatics, African public health and HIV/AIDS and bioethics were invited to participate. MEASURES: Perceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions. RESULTS: Of the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners. CONCLUSION: Experts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA.
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spelling pubmed-83202452021-08-02 Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study Nichol, Ariadne A Bendavid, Eran Mutenherwa, Farirai Patel, Chirag Cho, Mildred K BMJ Open Ethics OBJECTIVE: To better understand diverse experts’ views about the ethical implications of ongoing research funded by the National Institutes of Health that uses machine learning to predict HIV/AIDS risk in sub-Saharan Africa (SSA) based on publicly available Demographic and Health Surveys data. DESIGN: Three rounds of semi-structured surveys in an online expert panel using a modified Delphi approach. PARTICIPANTS: Experts in informatics, African public health and HIV/AIDS and bioethics were invited to participate. MEASURES: Perceived importance of or agreement about relevance of ethical issues on 5-point unipolar Likert scales. Qualitative data analysis identified emergent themes related to ethical issues and development of an ethical framework and recommendations for open-ended questions. RESULTS: Of the 35 invited experts, 22 participated in the online expert panel (63%). Emergent themes were the inclusion of African researchers in all aspects of study design, analysis and dissemination to identify and address local contextual issues, as well as engagement of communities. Experts focused on engagement with health and science professionals to address risks, benefits and communication of findings. Respondents prioritised the mitigation of stigma to research participants but recognised trade-offs between privacy and the need to disseminate findings to realise public health benefits. Strategies for responsible communication of results were suggested, including careful word choice in presentation of results and limited dissemination to need-to-know stakeholders such as public health planners. CONCLUSION: Experts identified ethical issues specific to the African context and to research on sensitive, publicly available data and strategies for addressing these issues. These findings can be used to inform an ethical implementation framework with research stage-specific recommendations on how to use publicly available data for machine learning-based predictive analytics to predict HIV/AIDS risk in SSA. BMJ Publishing Group 2021-07-28 /pmc/articles/PMC8320245/ /pubmed/34321310 http://dx.doi.org/10.1136/bmjopen-2021-052287 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) .
spellingShingle Ethics
Nichol, Ariadne A
Bendavid, Eran
Mutenherwa, Farirai
Patel, Chirag
Cho, Mildred K
Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title_full Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title_fullStr Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title_full_unstemmed Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title_short Diverse experts’ perspectives on ethical issues of using machine learning to predict HIV/AIDS risk in sub-Saharan Africa: a modified Delphi study
title_sort diverse experts’ perspectives on ethical issues of using machine learning to predict hiv/aids risk in sub-saharan africa: a modified delphi study
topic Ethics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320245/
https://www.ncbi.nlm.nih.gov/pubmed/34321310
http://dx.doi.org/10.1136/bmjopen-2021-052287
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