Join us for Manchester Institute of Innovation Research Seminar Series 20/21, webinar hosted by guest speakers, Roman Jurowetzki and Daniel Hain.
Register to attend via Eventbrite: https://www.eventbrite.co.uk/e/manchester-institute-of-innovation-research-roman-jurowetzki-daniel-hain-tickets-150495302501
The Privatization of AI Research(-ers):
Causes and Potential Consequences - From university-industry interaction to public research brain-drain?
Abstract:
The private sector is playing an increasingly important role in basic Artificial Intelligence (AI) R&D. This phenomenon, which is reflected in the perception of a brain drain of researchers from academia to industry, is raising concerns about a privatisation of AI research which could constrain its societal benefits. We contribute to the evidence base by quantifying transition flows between industry and academia and studying its drivers and potential consequences. We find a growing net flow of researchers from academia to industry, particularly from elite institutions into technology companies such as Google, Microsoft and Facebook. Our survival regression analysis reveals that researchers working in the field of deep learning as well as those with higher average impact are more likely to transition into industry. A difference-in-differences analysis of the effect of switching into industry on a researcher's influence proxied by citations indicates that an initial increase in impact declines as researchers spend more time in industry. This points at a privatisation of AI knowledge compared to a counterfactual where those high-impact researchers had remained in academia. Our findings highlight the importance of strengthening the public AI research sphere in order to ensure that the future of this powerful technology is not dominated by private interests.
• Does the private sector "harvest" the best performing AI researchers?
• After transitioning into industry, does their (publicly available) output decline?
• What does that mean for public interest, ethics, fairness and safety in emerging AI technologies?
Roman Jurowetzki, Associate Professor of Innovation Studies and Applied Data Science at Aalborg University, Denmark. Roman has been working with computational methods and particularly Natural Language Processing since his PhD work in 2012. Roman’s research focuses on technological change, in particular AI technology and its application. Since 2021 he is the lead of the AI:Denmark project – a national programme supporting adoption of AI technologies in SMEs as well as lead of the AI Growth Lab at Aalborg University.
He has co-founded the Social Data Science semester at AAU which introduces social science, business and econ. students to Machine Learning and AI methods. Since 2020 the program is also thought at Copenhagen Business School. In addition to graduate programs he has been disseminating ML and NLP techniques in at conferences (DRUID, AOM Big Data) at PhD courses and in the context of executive training.
Daniel Hain is an Associate Professor in Innovation Economics & Data Science at the Aalborg University Business School. His research is dedicated to the development and application of data-driven methods to map, understand, and predict technological change, and its causes and consequences for socioeconomic systems on various levels of aggregation. His current contextual focus is the dynamics of AI research and industry. Daniel is actively engaged in initiatives to educate (social science) students, professionals, and policymakers in understanding, evaluating, and applying modern Data Science and Artificial Intelligence methods for data-driven decision making.
