Big Tech-Funded AI Papers Have Higher Citation Impact, Greater Insularity, and Larger Recency Bias
Max Martin Gnewuch, Jan Philip Wahle, Terry Ruas, Bela Gipp
TL;DR
The paper investigates how Big Tech funding shapes AI research at leading conferences by constructing a Scopus‑based dataset of about 49.8k AI papers (1998–2022) and reading funding acknowledgments to classify industry, public, and non‑funded papers. It introduces the Citation Preference Ratio (CPR) and related metrics (ORCP, mAoC) to quantify citation behavior, insularity, and recency biases across funding types. Key findings include a marked rise in industry funding since 2015, a higher proportion of industry‑funded papers achieving high citation impact, and increasingly insular citation patterns that favor recent industry‑funded work. The work highlights potential shifts in how Big Tech influences AI research trajectories and underscores the need for transparent funding, open science, and diversified funding to sustain innovation.
Abstract
Over the past four decades, artificial intelligence (AI) research has flourished at the nexus of academia and industry. However, Big Tech companies have increasingly acquired the edge in computational resources, big data, and talent. So far, it has been largely unclear how many papers the industry funds, how their citation impact compares to non-funded papers, and what drives industry interest. This study fills that gap by quantifying the number of industry-funded papers at 10 top AI conferences (e.g., ICLR, CVPR, AAAI, ACL) and their citation influence. We analyze about 49.8K papers, about 1.8M citations from AI papers to other papers, and about 2.3M citations from other papers to AI papers from 1998-2022 in Scopus. Through seven research questions, we examine the volume and evolution of industry funding in AI research, the citation impact of funded papers, the diversity and temporal range of their citations, and the subfields in which industry predominantly acts. Our findings reveal that industry presence has grown markedly since 2015, from less than 2 percent to more than 11 percent in 2020. Between 2018 and 2022, 12 percent of industry-funded papers achieved high citation rates as measured by the h5-index, compared to 4 percent of non-industry-funded papers and 2 percent of non-funded papers. Top AI conferences engage more with industry-funded research than non-funded research, as measured by our newly proposed metric, the Citation Preference Ratio (CPR). We show that industry-funded research is increasingly insular, citing predominantly other industry-funded papers while referencing fewer non-funded papers. These findings reveal new trends in AI research funding, including a shift towards more industry-funded papers and their growing citation impact, greater insularity of industry-funded work than non-funded work, and a preference of industry-funded research to cite recent work.