Capraro Valerio, Lentsch Austin, Acemoglu Daron, Akgun Selin, Akhmedova Aisel, Bilancini Ennio, Bonnefon Jean-François, Brañas-Garza Pablo, Butera Luigi, Douglas Karen M, Everett Jim A C, Gigerenzer Gerd, Greenhow Christine, Hashimoto Daniel A, Holt-Lunstad Julianne, Jetten Jolanda, Johnson Simon, Kunz Werner H, Longoni Chiara, Lunn Pete, Natale Simone, Paluch Stefanie, Rahwan Iyad, Selwyn Neil, Singh Vivek, Suri Siddharth, Sutcliffe Jennifer, Tomlinson Joe, van der Linden Sander, Van Lange Paul A M, Wall Friederike, Van Bavel Jay J, Viale Riccardo
Department of Psychology, University of Milan-Bicocca, Milan 20126, Italy.
Department of Economics, MIT, Cambridge, MA 02142, USA.
PNAS Nexus. 2024 Jun 11;3(6):pgae191. doi: 10.1093/pnasnexus/pgae191. eCollection 2024 Jun.
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 domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the , it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In , it offers personalized learning, but may widen the digital divide. In , 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.
生成式人工智能（AI）有可能加剧和改善现有的社会经济不平等现象。在本文中，我们提供了一份关于生成式人工智能对（错误）信息以及三个信息密集型领域（工作、教育和医疗保健）潜在影响的跨学科综述。我们的目标是强调生成式人工智能如何可能加剧现有不平等现象，同时阐明人工智能如何有助于缓解普遍存在的社会问题。在内容创作领域，生成式人工智能可以使内容创作和获取民主化，但可能会大幅扩大错误信息的产生和传播。在工作领域，它可以提高生产力并创造新的就业机会，但好处可能分配不均。在教育领域，它提供个性化学习，但可能会扩大数字鸿沟。在医疗保健领域，它可能会改善诊断和可及性，但可能会加深现有的不平等现象。在每个部分，我们涵盖一个特定主题，评估现有研究，找出关键差距，并推荐研究方向，包括使先验假设推导复杂化的明确权衡。我们在结尾部分强调政策制定在最大化生成式人工智能减少不平等现象的潜力同时减轻其有害影响方面的作用。我们讨论了欧盟、美国和英国现有政策框架的优缺点，发现每个框架都未能充分应对我们所确定的社会经济挑战。我们提出了几项具体政策，这些政策可以通过推进生成式人工智能来促进共同繁荣。本文强调需要跨学科合作来理解和应对生成式人工智能带来的复杂挑战。
