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arXiv · 2312.12881

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
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Abstract

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 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 equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.

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Stanisław Giziński, Paulina Kaczyńska, Hubert Ruczyński, Emilia Wiśnios, Bartosz Pieliński, Przemysław Biecek, Julian Sienkiewicz. 2023-12-20. Big Tech influence over AI research revisited: memetic analysis of attribution of ideas to affiliation. https://doi.org/10.1016/j.joi.2024.101572

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