How will generative AI affect socio-economic inequalities?
This review was led by Valerio Capraro, with many experts including the project lead Jim Everett, Advisory Board Members Jean Francois Bonnefon, and Iyad Rahwan, and also from Kent, Karen Douglas. In this paper, the authors draw on a wide body of research to consider whether generative AI tools will help or harm society, especially when it comes to jobs, education, healthcare, and the information humans rely on. This paper highlights that without effective policies, AI may intensify current inequalities, prompting a call for action to ensure the technology supports all members of society.
Jim described working on this paper:
“This was a fascinating paper to work on with such world-leading experts across a range of disciplines with different expertise. While many of us may have different perspectives on how optimistic or pessimistic we are about the prospects of AI on inequalities, the work we have reviewed here makes it clear that this is a critical question that we need to grapple with.”
Read More:
Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., … Everett, J.A.C.,… & Viale, R. (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS nexus, 3(6)
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 emphasises the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.
Read the full article here: https://pubmed.ncbi.nlm.nih.gov/38864006/