The impact of generative artificial intelligence on socioeconomic inequalities and policy making

被引：59

作者：

Capraro, Valerio ^{[1
]}

Lentsch, Austin ^{[2
]}

Acemoglu, Daron ^{[3
]}

Akgun, Selin ^{[4
]}

Akhmedova, Aisel ^{[4
]}

Bilancini, Ennio ^{[5
]}

Bonnefon, Jean-Francois ^{[6
]}

Branas-Garza, Pablo ^{[7
]}

Butera, Luigi ^{[8
]}

Douglas, Karen M. ^{[9
]}

Everett, Jim A. C. ^{[10
]}

Gigerenzer, Gerd ^{[11
]}

Greenhow, Christine ^{[4
]}

Hashimoto, Daniel A. ^{[12
,13
]}

Holt-Lunstad, Julianne ^{[14
]}

Jetten, Jolanda ^{[15
]}

Johnson, Simon ^{[16
]}

Kunz, Werner H. ^{[17
]}

Longoni, Chiara ^{[18
]}

Lunn, Pete ^{[19
]}

Natale, Simone ^{[20
]}

Paluch, Stefanie ^{[21
]}

Rahwan, Iyad ^{[22
]}

Selwyn, Neil ^{[23
]}

Singh, Vivek ^{[13
]}

Suri, Siddharth ^{[24
]}

Sutcliffe, Jennifer ^{[4
]}

Tomlinson, Joe ^{[25
]}

van der Linden, Sander ^{[26
]}

Van Lange, Paul A. M. ^{[27
]}

Wall, Friederike ^{[28
]}

Van Bavel, Jay J. ^{[29
,30
]}

Viale, Riccardo ^{[31
]}

机构：

[1] Univ Milano Bicocca, Dept Psychol, I-20126 Milan, Italy

[2] MIT, Dept Econ, Cambridge, MA 02142 USA

[3] MIT, Inst Prof & Dept Econ, Cambridge, MA 02142 USA

[4] Michigan State Univ, Coll Educ, E Lansing, MI 48824 USA

[5] IMT Sch Adv Studies Lucca, I-55100 Lucca, Italy

[6] Toulouse Sch Econ, F-31000 Toulouse, France

[7] Loyola Andalucia Univ, Loyola Behav Lab, Cordoba 41740, Spain

[8] Copenhagen Business Sch, Dept Econ, DK-2000 Frederiksberg, Denmark

[9] Univ Kent, Sch Psychol, Canterbury CT27NP, England

[10] Max Planck Inst Human Dev, D-14195 Berlin, Germany

[11] Univ Penn, Perelman Sch Med, Dept Surg, Penn Comp Assisted Surg & Outcomes Lab, Philadelphia, PA 19104 USA

[12] Univ Penn, Sch Engn & Appl Sci, Dept Comp & Informat Sci, Philadelphia, PA 19104 USA

[13] Brigham Young Univ, Dept Psychol & Neurosci, Provo, UT 84602 USA

[14] Univ Queensland, Sch Psychol, St Lucia, Qld 4067, Australia

[15] MIT Sloan Sch Management, Sch Management, Cambridge, MA 02142 USA

[16] Univ Massachusetts, Dept Mkt, Boston, MA 02125 USA

[17] Bocconi Univ, Dept Mkt, I-20136 Milan, Italy

[18] Econ & Social Res Inst, Behav Res Unit, Dublin D02 K138, Ireland

[19] Univ Turin, Dept Humanities, I-10125 Turin, Italy

[20] Aarhus Univ, Dept Serv & Technol Mkt, DK-8000 Aarhus, Denmark

[21] Max Planck Inst Human Dev, Ctr Humans & Machines, D-14195 Berlin, Germany

[22] Monash Univ, Fac Educ, Clayton, VIC 3168, Australia

[23] Microsoft Res, Redmond, WA 98502 USA

[24] Univ York, York Law Sch, York YO10 5DD, England

[25] Univ Cambridge, Dept Psychol, Cambridge CB2 1TN, England

[26] Vrije Univ, Dept Expt & Appl Psychol, NL-1081HV Amsterdam, Netherlands

[27] Univ Klagenfurt, Dept Management Control & Strateg Management, A-9020 Klagenfurt Worthersee, Austria

[28] NYU, Ctr Neural Sci, Dept Psychol, New York, NY 10012 USA

[29] Norwegian Sch Econ, N-5045 Bergen, Norway

[30] Univ Milano Bicocca, CISEPS, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy

[31] Herbert Simon Soc, I-10122 Turin, Italy

来源：

PNAS NEXUS | 2024年 / 3卷 / 06期

基金：

美国国家科学基金会; 澳大利亚研究理事会; 欧洲研究理事会;

关键词：

HEALTH-CARE; MODEL; AI; DISPARITIES; TECHNOLOGY; AUTOMATION; MEDICINE; IDENTITY; TASKS; TRUST;

D O I：

10.1093/pnasnexus/pgae191

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

引用

页数：18

共 158 条

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