Fair and equitable AI in biomedical research and healthcare: Social science perspectives

Renate Baumgartner; Payal Arora; Corinna Bath; Darja Burljaev; Kinga Ciereszko; Bart Custers; Jin Ding; Waltraud Ernst; Eduard Fosch-Villaronga; Vassilis Galanos; Thomas Gremsl; Tereza Hendl; Cordula Kropp; Christian Lenk; Paul Martin; Somto Mbelu; Sara Morais dos Santos Bruss; Karolina Napiwodzka; Ewa Nowak; Tiara Roxanne; Silja Samerski; David Schneeberger; Karolin Tampe-Mai; Katerina Vlantoni; Kevin Wiggert; Robin Williams

doi:10.1016/j.artmed.2023.102658

Fair and equitable AI in biomedical research and healthcare: Social science perspectives

Renate Baumgartner^*
, Payal Arora
, Corinna Bath
, Darja Burljaev
, Kinga Ciereszko
, Bart Custers
, Jin Ding
, Waltraud Ernst
, Eduard Fosch-Villaronga
, Vassilis Galanos
, Thomas Gremsl
, Tereza Hendl
, Cordula Kropp
, Christian Lenk
, Paul Martin
, Somto Mbelu
, Sara Morais dos Santos Bruss
, Karolina Napiwodzka
, Ewa Nowak
, Tiara Roxanne

Silja Samerski, David Schneeberger, Karolin Tampe-Mai, Katerina Vlantoni, Kevin Wiggert, Robin Williams

^*Corresponding author for this work

University of Tübingen
Vrije Universiteit Amsterdam
University of Braunschweig
Adam Mickiewicz University in Poznań
Leiden University
University of Sheffield
Johannes Kepler University Linz
University of Edinburgh
University of Graz
Augsburg University
Ludwig Maximilian University of Munich
University of Stuttgart
Ulm University
Haus der Kulturen der Welt
Data & Society Institute
University of Applied Sciences Emden/Leer
Medical University of Graz
National and Kapodistrian University of Athens
Technical University of Berlin

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

Original language	English
Article number	102658
Journal	Artificial Intelligence in Medicine
Volume	144
DOIs	https://doi.org/10.1016/j.artmed.2023.102658
Publication status	Published - Oct 2023

Bibliographical note

Funding: This work was supported by the Wellcome Trust [grant number 219875/Z/19/Z]; the BMBF [grant number FKZ 01GP1791]; acatech NATIONAL ACADEMY OF SCIENCE AND ENGINEERING and Körber Stiftung; the FWF [project P-32554 “A reference model of explainable Artificial Intelligence for the Medical Domain”]; the United Kingdom Research and Innovation: Trusted Autonomous Systems Programme [grant number EP/V026607/1]. EFV would like to acknowledge that this collaborative paper is part of the Safe and Sound project, a project that has received funding from the European Union's Horizon-ERC program Grant Agreement No. 101076929. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them.

Publisher Copyright: © 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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1-s2.0-S0933365723001720-mainFinal published version, 598 KBLicence: CC BY

Cite this

Baumgartner, R., Arora, P., Bath, C., Burljaev, D., Ciereszko, K., Custers, B., Ding, J., Ernst, W., Fosch-Villaronga, E., Galanos, V., Gremsl, T., Hendl, T., Kropp, C., Lenk, C., Martin, P., Mbelu, S., Morais dos Santos Bruss, S., Napiwodzka, K., Nowak, E., ... Williams, R. (2023). Fair and equitable AI in biomedical research and healthcare: Social science perspectives. Artificial Intelligence in Medicine, 144, Article 102658. https://doi.org/10.1016/j.artmed.2023.102658

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title = "Fair and equitable AI in biomedical research and healthcare: Social science perspectives",

abstract = "Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference {\textquotedblleft}Fair medicine and AI{\textquotedblright} (online 3{\textendash}5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.",

author = "Renate Baumgartner and Payal Arora and Corinna Bath and Darja Burljaev and Kinga Ciereszko and Bart Custers and Jin Ding and Waltraud Ernst and Eduard Fosch-Villaronga and Vassilis Galanos and Thomas Gremsl and Tereza Hendl and Cordula Kropp and Christian Lenk and Paul Martin and Somto Mbelu and \{Morais dos Santos Bruss\}, Sara and Karolina Napiwodzka and Ewa Nowak and Tiara Roxanne and Silja Samerski and David Schneeberger and Karolin Tampe-Mai and Katerina Vlantoni and Kevin Wiggert and Robin Williams",

note = "Funding: This work was supported by the Wellcome Trust [grant number 219875/Z/19/Z]; the BMBF [grant number FKZ 01GP1791]; acatech NATIONAL ACADEMY OF SCIENCE AND ENGINEERING and K{\"o}rber Stiftung; the FWF [project P-32554 {\textquotedblleft}A reference model of explainable Artificial Intelligence for the Medical Domain{\textquotedblright}]; the United Kingdom Research and Innovation: Trusted Autonomous Systems Programme [grant number EP/V026607/1]. EFV would like to acknowledge that this collaborative paper is part of the Safe and Sound project, a project that has received funding from the European Union's Horizon-ERC program Grant Agreement No. 101076929. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Publisher Copyright: {\textcopyright} 2023",

year = "2023",

month = oct,

doi = "10.1016/j.artmed.2023.102658",

language = "English",

volume = "144",

journal = "Artificial Intelligence in Medicine",

issn = "0933-3657",

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Baumgartner, R, Arora, P, Bath, C, Burljaev, D, Ciereszko, K, Custers, B, Ding, J, Ernst, W, Fosch-Villaronga, E, Galanos, V, Gremsl, T, Hendl, T, Kropp, C, Lenk, C, Martin, P, Mbelu, S, Morais dos Santos Bruss, S, Napiwodzka, K, Nowak, E, Roxanne, T, Samerski, S, Schneeberger, D, Tampe-Mai, K, Vlantoni, K, Wiggert, K & Williams, R 2023, 'Fair and equitable AI in biomedical research and healthcare: Social science perspectives', Artificial Intelligence in Medicine, vol. 144, 102658. https://doi.org/10.1016/j.artmed.2023.102658

TY - JOUR

T1 - Fair and equitable AI in biomedical research and healthcare

T2 - Social science perspectives

AU - Baumgartner, Renate

AU - Arora, Payal

AU - Bath, Corinna

AU - Burljaev, Darja

AU - Ciereszko, Kinga

AU - Custers, Bart

AU - Ding, Jin

AU - Ernst, Waltraud

AU - Fosch-Villaronga, Eduard

AU - Galanos, Vassilis

AU - Gremsl, Thomas

AU - Hendl, Tereza

AU - Kropp, Cordula

AU - Lenk, Christian

AU - Martin, Paul

AU - Mbelu, Somto

AU - Morais dos Santos Bruss, Sara

AU - Napiwodzka, Karolina

AU - Nowak, Ewa

AU - Roxanne, Tiara

AU - Samerski, Silja

AU - Schneeberger, David

AU - Tampe-Mai, Karolin

AU - Vlantoni, Katerina

AU - Wiggert, Kevin

AU - Williams, Robin

N1 - Funding: This work was supported by the Wellcome Trust [grant number 219875/Z/19/Z]; the BMBF [grant number FKZ 01GP1791]; acatech NATIONAL ACADEMY OF SCIENCE AND ENGINEERING and Körber Stiftung; the FWF [project P-32554 “A reference model of explainable Artificial Intelligence for the Medical Domain”]; the United Kingdom Research and Innovation: Trusted Autonomous Systems Programme [grant number EP/V026607/1]. EFV would like to acknowledge that this collaborative paper is part of the Safe and Sound project, a project that has received funding from the European Union's Horizon-ERC program Grant Agreement No. 101076929. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. Publisher Copyright: © 2023

PY - 2023/10

Y1 - 2023/10

N2 - Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

AB - Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference “Fair medicine and AI” (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation.

UR - https://www.scopus.com/pages/publications/85172993452

U2 - 10.1016/j.artmed.2023.102658

DO - 10.1016/j.artmed.2023.102658

M3 - Article

C2 - 37783540

AN - SCOPUS:85172993452

SN - 0933-3657

VL - 144

JO - Artificial Intelligence in Medicine

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ER -

Fair and equitable AI in biomedical research and healthcare: Social science perspectives

Abstract

Bibliographical note

UN SDGs

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