TY - JOUR
T1 - The Impact of Generative Artificial Intelligence on Socioeconomic Inequalities and Policy Making
AU - Capraro, Valerio
AU - Lentsch, Austin
AU - Acemoglu, Daron
AU - Akgun, Selin
AU - Akhmedova, Aisel
AU - Bilancini, Ennio
AU - Bonnefon, Jean-François
AU - Brañas-Garza, Pablo
AU - Butera, Luigi
AU - Douglas, Karen M.
AU - Everett, Jim A. C.
AU - Gigerenzer, Gerd
AU - Greenhow, Christine
AU - Hashimoto, Daniel A.
AU - Holt-Lunstad, Julianne
AU - Jetten, Jolanda
AU - Johnson, Simon
AU - Kunz , Werner H.
AU - Longoni, Chiara
AU - Lunn, Pete
AU - Natale, Simone
AU - Paluch, Stefanie
AU - Rahwan, Iyad
AU - Selwyn, Neil
AU - Singh, Vivek
AU - Suri, Siddharth
AU - Sutcliffe, Jennifer
AU - Tomlinson, Joe
AU - Linden, Sander van der
AU - Van Lange, Paul A. M.
AU - Wall, Friederike
AU - Van Bavel, Jay J.
AU - Viale, Riccardo
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
U2 - 10.1093/pnasnexus/pgae191
DO - 10.1093/pnasnexus/pgae191
M3 - Journal article
SN - 2752-6542
VL - 3
JO - PNAS Nexus
JF - PNAS Nexus
IS - 6
M1 - pgae191
ER -