  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.
