{"title":"Use of exclusive data for corporate research on machine learning and artificial intelligence: Implications for innovation and competition policy","authors":"Seokbeom Kwon , Alan L. Porter","doi":"10.1016/j.techsoc.2025.102820","DOIUrl":null,"url":null,"abstract":"<div><div>Corporate research has been a primary driver of recent advances in Machine Learning and Artificial Intelligence (ML/AI). The present study contends that firms' prominent role in advancing the ML/AI research field is partly attributed to their use of exclusive data for ML/AI research. Using data on nearly 8000 preprints of ML/AI research papers archived in arXiv and the performance of their proposed algorithms, we found multifaceted evidence that corporate ML/AI research has exhibited a particularly significant citation impact compared to non-corporate research. Importantly, we showed that the significance of corporate research is more pronounced when it originates from the use of exclusive data. We argue that firms' use of exclusive data has been instrumental in not only encouraging their research on ML/AI, but also enhancing the research impact, which we call the “dual role” of the data in corporate research on ML/AI. In light of the policy concern regarding the potential anticompetitive implications of firms' utilization of data exclusivity in the evolving landscape of ML/AI, our conclusion calls for a comprehensive policy discourse on the consequences of firms' exclusive use of data for their ML/AI research within broader dimensions of societal welfare, including innovation and competition.</div></div>","PeriodicalId":47979,"journal":{"name":"Technology in Society","volume":"81 ","pages":"Article 102820"},"PeriodicalIF":10.1000,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology in Society","FirstCategoryId":"90","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0160791X25000107","RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL ISSUES","Score":null,"Total":0}
引用次数: 0
Abstract
Corporate research has been a primary driver of recent advances in Machine Learning and Artificial Intelligence (ML/AI). The present study contends that firms' prominent role in advancing the ML/AI research field is partly attributed to their use of exclusive data for ML/AI research. Using data on nearly 8000 preprints of ML/AI research papers archived in arXiv and the performance of their proposed algorithms, we found multifaceted evidence that corporate ML/AI research has exhibited a particularly significant citation impact compared to non-corporate research. Importantly, we showed that the significance of corporate research is more pronounced when it originates from the use of exclusive data. We argue that firms' use of exclusive data has been instrumental in not only encouraging their research on ML/AI, but also enhancing the research impact, which we call the “dual role” of the data in corporate research on ML/AI. In light of the policy concern regarding the potential anticompetitive implications of firms' utilization of data exclusivity in the evolving landscape of ML/AI, our conclusion calls for a comprehensive policy discourse on the consequences of firms' exclusive use of data for their ML/AI research within broader dimensions of societal welfare, including innovation and competition.
期刊介绍:
Technology in Society is a global journal dedicated to fostering discourse at the crossroads of technological change and the social, economic, business, and philosophical transformation of our world. The journal aims to provide scholarly contributions that empower decision-makers to thoughtfully and intentionally navigate the decisions shaping this dynamic landscape. A common thread across these fields is the role of technology in society, influencing economic, political, and cultural dynamics. Scholarly work in Technology in Society delves into the social forces shaping technological decisions and the societal choices regarding technology use. This encompasses scholarly and theoretical approaches (history and philosophy of science and technology, technology forecasting, economic growth, and policy, ethics), applied approaches (business innovation, technology management, legal and engineering), and developmental perspectives (technology transfer, technology assessment, and economic development). Detailed information about the journal's aims and scope on specific topics can be found in Technology in Society Briefings, accessible via our Special Issues and Article Collections.