Big Tech influence over AI research revisited: memetic analysis of
  attribution of ideas to affiliation
Stanisław Giziński Paulina Kaczyńska Hubert Ruczyński Emilia Wiśnios Bartosz Pieliński Przemysław Biecek Julian Sienkiewicz
There exists a growing discourse around the domination of Big Tech on the
landscape of artificial intelligence (AI) research, yet our comprehension of
this phenomenon remains cursory. This paper aims to broaden and deepen our
understanding of Big Tech's reach and power within AI research. It highlights
the dominance not merely in terms of sheer publication volume but rather in the
propagation of new ideas or \textit{memes}. Current studies often oversimplify
the concept of influence to the share of affiliations in academic papers,
typically sourced from limited databases such as arXiv or specific academic
conferences.
  The main goal of this paper is to unravel the specific nuances of such
influence, determining which AI ideas are predominantly driven by Big Tech
entities. By employing network and memetic analysis on AI-oriented paper
abstracts and their citation network, we are able to grasp a deeper insight
into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are
able to perform such analysis on a much bigger scale than previous attempts.
  Our findings suggest, that while Big Tech-affiliated papers are
disproportionately more cited in some areas, the most cited papers are those
affiliated with both Big Tech and Academia. Focusing on the most contagious
memes, their attribution to specific affiliation groups (Big Tech, Academia,
mixed affiliation) seems to be equally distributed between those three groups.
This suggests that the notion of Big Tech domination over AI research is
oversimplified in the discourse.
  Ultimately, this more nuanced understanding of Big Tech's and Academia's
influence could inform a more symbiotic alliance between these stakeholders
which would better serve the dual goals of societal welfare and the scientific
integrity of AI research.