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Conceptualising fairness: three pillars for medical algorithms and health equity

OBJECTIVES: Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the...

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Autores principales: Sikstrom, Laura, Maslej, Marta M, Hui, Katrina, Findlay, Zoe, Buchman, Daniel Z, Hill, Sean L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753410/
https://www.ncbi.nlm.nih.gov/pubmed/35012941
http://dx.doi.org/10.1136/bmjhci-2021-100459
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author Sikstrom, Laura
Maslej, Marta M
Hui, Katrina
Findlay, Zoe
Buchman, Daniel Z
Hill, Sean L
author_facet Sikstrom, Laura
Maslej, Marta M
Hui, Katrina
Findlay, Zoe
Buchman, Daniel Z
Hill, Sean L
author_sort Sikstrom, Laura
collection PubMed
description OBJECTIVES: Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature. METHODS: We conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo). RESULTS: Our synthesis identified ‘Three Pillars for Fairness’: transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare. DISCUSSION: We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients. CONCLUSION: We conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare.
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spelling pubmed-87534102022-01-26 Conceptualising fairness: three pillars for medical algorithms and health equity Sikstrom, Laura Maslej, Marta M Hui, Katrina Findlay, Zoe Buchman, Daniel Z Hill, Sean L BMJ Health Care Inform Review OBJECTIVES: Fairness is a core concept meant to grapple with different forms of discrimination and bias that emerge with advances in Artificial Intelligence (eg, machine learning, ML). Yet, claims to fairness in ML discourses are often vague and contradictory. The response to these issues within the scientific community has been technocratic. Studies either measure (mathematically) competing definitions of fairness, and/or recommend a range of governance tools (eg, fairness checklists or guiding principles). To advance efforts to operationalise fairness in medicine, we synthesised a broad range of literature. METHODS: We conducted an environmental scan of English language literature on fairness from 1960-July 31, 2021. Electronic databases Medline, PubMed and Google Scholar were searched, supplemented by additional hand searches. Data from 213 selected publications were analysed using rapid framework analysis. Search and analysis were completed in two rounds: to explore previously identified issues (a priori), as well as those emerging from the analysis (de novo). RESULTS: Our synthesis identified ‘Three Pillars for Fairness’: transparency, impartiality and inclusion. We draw on these insights to propose a multidimensional conceptual framework to guide empirical research on the operationalisation of fairness in healthcare. DISCUSSION: We apply the conceptual framework generated by our synthesis to risk assessment in psychiatry as a case study. We argue that any claim to fairness must reflect critical assessment and ongoing social and political deliberation around these three pillars with a range of stakeholders, including patients. CONCLUSION: We conclude by outlining areas for further research that would bolster ongoing commitments to fairness and health equity in healthcare. BMJ Publishing Group 2022-01-10 /pmc/articles/PMC8753410/ /pubmed/35012941 http://dx.doi.org/10.1136/bmjhci-2021-100459 Text en © Author(s) (or their employer(s)) 2022. 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 Review
Sikstrom, Laura
Maslej, Marta M
Hui, Katrina
Findlay, Zoe
Buchman, Daniel Z
Hill, Sean L
Conceptualising fairness: three pillars for medical algorithms and health equity
title Conceptualising fairness: three pillars for medical algorithms and health equity
title_full Conceptualising fairness: three pillars for medical algorithms and health equity
title_fullStr Conceptualising fairness: three pillars for medical algorithms and health equity
title_full_unstemmed Conceptualising fairness: three pillars for medical algorithms and health equity
title_short Conceptualising fairness: three pillars for medical algorithms and health equity
title_sort conceptualising fairness: three pillars for medical algorithms and health equity
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8753410/
https://www.ncbi.nlm.nih.gov/pubmed/35012941
http://dx.doi.org/10.1136/bmjhci-2021-100459
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