The Emerging Generative Artificial Intelligence Divide in the United States

Madeleine I. G. Daepp, Scott Counts
4/18/2024
cs.AIcs.CY

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

The digital divide refers to disparities in access to and use of digital tooling across social and economic groups. This divide can reinforce marginalization both at the individual level and at the level of places, because persistent economic advantages accrue to places where new technologies are adopted early. To what extent are emerging generative artificial intelligence (AI) tools subject to these social and spatial divides? We leverage a large-scale search query database to characterize U.S. residents' knowledge of a novel generative AI tool, ChatGPT, during its first six months of release. We identify hotspots of higher-than-expected search volumes for ChatGPT in coastal metropolitan areas, while coldspots are evident in the American South, Appalachia, and the Midwest. Nationwide, counties with the highest rates of search have proportionally more educated and more economically advantaged populations, as well as proportionally more technology and finance-sector jobs in comparison with other counties or with the national average. Observed associations with race/ethnicity and urbanicity are attenuated in fully adjusted hierarchical models, but education emerges as the strongest positive predictor of generative AI awareness. In the absence of intervention, early differences in uptake show a potential to reinforce existing spatial and socioeconomic divides.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{daepp2024emerging,
  title  = {The Emerging Generative Artificial Intelligence Divide in the United States},
  author = {Madeleine I. G. Daepp and Scott Counts},
  year   = {2024},
  eprint = {2404.11988},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.11988v3}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2404.11988.svg)](https://codeofpaper.com/paper/2404.11988)

Discussion