Generative artificial intelligence 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.

翻译：生成式人工智能既可能加剧现有社会经济不平等，也有望缓解这些问题。本文通过跨学科前沿视角，系统梳理了生成式人工智能在（虚假）信息及三个信息密集型领域（工作、教育、医疗保健）中的潜在影响。研究旨在揭示生成式人工智能如何恶化既有不平等，同时阐明其可能帮助缓解普遍社会问题的路径。在信息领域，生成式人工智能可推动内容创作与获取的民主化，但可能大幅提升虚假信息的生成与传播规模；在工作场所，它能提升生产力并创造新型岗位，但收益分配可能呈现不均衡性；在教育领域，该技术可提供个性化学习方案，却可能扩大数字鸿沟；在医疗保健领域，虽能改善诊断能力与可及性，但可能加深既有健康不平等。每个章节聚焦特定议题，评估现有研究成果，识别关键研究空白，并提出研究方向建议——包括因复杂权衡关系而难以形成先验假设的显性矛盾。在结论部分，我们着重探讨政策制定如何最大化生成式人工智能降低不平等的能力，同时减轻其负面影响。通过分析欧盟、美国及英国现有政策框架的优缺点，指出这些体系均未能充分应对文中揭示的社会经济挑战。我们提出了若干具体政策建议，以期借助生成式人工智能的进步促进共同繁荣。本文强调，理解并应对生成式人工智能的复杂挑战亟需跨学科协作。