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

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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 languageEnglish
Article number102658
Pages (from-to)1-9
Number of pages9
JournalArtificial Intelligence in Medicine
Volume144
Early online date4 Sept 2023
DOIs
Publication statusPublished - Oct 2023

Bibliographical note

Funding Information:
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.

Funding Information:
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

Funding

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. 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.

FundersFunder number
Artificial Intelligence for the Medical Domain
European Union's Horizon-ERC101076929
Trusted Autonomous Systems ProgrammeEP/V026607/1
Wellcome Trust219875/Z/19/Z
Wellcome Trust
European Research Council
Bundesministerium für Bildung und ForschungFKZ 01GP1791
Bundesministerium für Bildung und Forschung
Austrian Science FundP-32554
Austrian Science Fund
Körber-Stiftung

    Keywords

    • Bias
    • Discrimination
    • Health equity
    • Inequalities
    • Medicine

    Fingerprint

    Dive into the research topics of 'Fair and equitable AI in biomedical research and healthcare: Social science perspectives'. Together they form a unique fingerprint.

    Cite this