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.
PY - 2023/10/1
Y1 - 2023/10/1
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.
KW - bias
KW - discrimination
KW - health equity
KW - inequalities
KW - medicine
UR - http://www.scopus.com/inward/record.url?scp=85172993452&partnerID=8YFLogxK
UR - https://www.sciencedirect.com/journal/artificial-intelligence-in-medicine
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
SP - 1
EP - 9
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 102658
ER -