🔥Now in press at
@PNASNexus 🔥
We provide a state-of-the-art, interdisciplinary overview of generative AI’s potential impacts 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 each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions.
We pose 72 specific research questions that could significantly advance the field. Each question is non-trivial, and we discuss explicit trade-offs complicating the derivation of a priori hypotheses. See tables 1-4 in the paper and S1-S4 in the SI.
We conclude with a section highlighting the role of policymaking in maximizing generative AI’s potential to reduce inequalities while mitigating its harmful effects.
We discuss the strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, noting that each fails to fully address the socioeconomic challenges we have identified.
We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. See table 5 in the paper.
We hope that this work contributes to a comprehensive research agenda and public debates on these critical topics.
Full paper:
papers.ssrn.com/sol3/papers.…
This article would not have been possible without the collaboration of an amazing, multidisciplinary group of authors. Immense thanks to everyone involved!
@AustinLentsch @DAcemogluMIT @SelinAkgun9 Aisel Akhmedova
@EBilancini @JFBonnefon @BehSnaps
@lu_butera @Karen_Douglas Jim Everett Gerd Gigerenzer
@chrisgreenhow @Laparoscopes @PCASOLab @jholtlunstad @jetten_j @baselinescene @werkunz @longoni_chiara Pete Lunn
@simone_natale Stefanie Paluch
@iyadrahwan Neil Selwyn Vivek Singh
@ssuri Jennifer Sutcliffe @JoePTomlinson
@Sander_vdLinden @PaulvanLange @FriederikeWall @jayvanbavel Riccardo Viale