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

被引：21

作者：

Baumgartner, Renate ^{[1
,2
]}

Arora, Payal ^{[3
]}

Bath, Corinna ^{[4
]}

Burljaev, Darja ^{[1
]}

Ciereszko, Kinga ^{[5
]}

Custers, Bart ^{[6
]}

Ding, Jin ^{[7
,8
]}

Ernst, Waltraud ^{[9
]}

Fosch-Villaronga, Eduard ^{[6
]}

Galanos, Vassilis ^{[10
]}

Gremsl, Thomas ^{[11
]}

Hendl, Tereza ^{[12
,13
]}

Kropp, Cordula ^{[14
]}

Lenk, Christian ^{[15
]}

Martin, Paul ^{[7
,8
]}

Mbelu, Somto ^{[16
]}

Bruss, Sara Morais dos Santos ^{[17
]}

Napiwodzka, Karolina ^{[5
]}

Nowak, Ewa ^{[5
]}

Roxanne, Tiara ^{[18
]}

Samerski, Silja ^{[19
]}

Schneeberger, David ^{[20
]}

Tampe-Mai, Karolin ^{[14
]}

Vlantoni, Katerina ^{[21
]}

Wiggert, Kevin ^{[22
]}

Williams, Robin ^{[10
]}

机构：

[1] Univ Tubingen, Ctr Gender & Divers Res, Wilhelmstr 56, D-72074 Tubingen, Germany

[2] Vrije Univ Amsterdam, Athena Inst, Boelelaan 1085, NL-1081 HV Amsterdam, Netherlands

[3] Erasmus Univ, Erasmus Sch Philosophy, Burgemeester Oudlaan 50, NL-3062 PA Rotterdam, Netherlands

[4] TU Braunschweig, Inst Flight Guidance, Gender Technol & Mobil, Hermann Blenk Str 27, D-38108 Braunschweig, Germany

[5] Adam Mickiewicz Univ, Dept Philosophy, Szamarzewski St 89C, PL-60569 Poznan, Poland

[6] Leiden Univ, eLaw Ctr Law & Digital Technol, Steenschuur 25, NL-2311 ES Leiden, Netherlands

[7] Univ Sheffield, iHuman, ICOSS, 219 Portobello, Sheffield S1 4DP, England

[8] Univ Sheffield, Dept Sociol Studies, ICOSS, 219 Portobello, Sheffield S1 4DP, England

[9] Johannes Kepler Univ Linz, Inst Womens & Gender Studies, Altenberger Str 69, A-4040 Linz, Austria

[10] Univ Edinburgh, Sch Social & Polit Sci, Sci Technol & Innovat Studies, Old Surgeons Hall, Edinburgh EH1 1LZ, Scotland

[11] Karl Franzens Univ Graz, Inst Ethics & Social Teaching, Fac Catholic Theol, Heinrichstr 78b-2, A-8010 Graz, Austria

[12] Univ Augsburg, Professorship Ethics Med, Stenglinstr 2, D-86156 Augsburg, Germany

[13] Ludwig Maximilians Univ Munchen, Inst Ethics Hist & Theory Med, Lessingstr 2, D-80336 Munich, Germany

[14] Univ Stuttgart, Ctr Interdisciplinary Risk & Innovat Studies ZIRIU, Seidenstr 36, D-70174 Stuttgart, Germany

[15] Ulm Univ, Inst Hist Philosophy & Ethics Med, Parkstra 11, D-89073 Ulm, Germany

[16] Erasmus Univ, Erasmus Sch Philosophy, 10A Ademola Close Remi Fani Kayode St, Lagos, Nigeria

[17] Haus Kulturen Welt HKW, John Foster Dulles Allee 10, D-10557 Berlin, Germany

[18] Data & Soc Inst, 228 Pk Ave S PMB 83075, New York, NY 10003 USA

[19] Hsch Emden Leer, Fachbereich Soziale Arbeit & Gesundheit, Constantiapl 4, D-26723 Emden, Germany

[20] Med Univ Graz, Inst Med Informat Stat & Documentat, Auenbruggerpl 2, A-8036 Graz, Austria

[21] Natl & Kapodistrian Univ Athens, Sch Sci, Dept Hist & Philosophy Sci, Athens 15771, Greece

[22] Tech Univ Berlin, Inst Sociol, Dept Sociol Technol & Innovat, Fraunhoferstr 33-36, D-10623 Berlin, Germany

来源：

ARTIFICIAL INTELLIGENCE IN MEDICINE | 2023年 / 144卷

基金：

英国惠康基金;

关键词：

Inequalities; Health equity; Medicine; Discrimination; Bias; ARTIFICIAL-INTELLIGENCE; RACIAL BIAS; EXPECTATIONS; TECHNOLOGY; LESSONS; AGE;

D O I：

10.1016/j.artmed.2023.102658

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

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