Crowds and machines: AI as a manager in large-scale science and innovation projects
Artificial intelligence (AI) can make important contributions to scientific research by performing functional tasks such as reviewing prior literature, classifying digital data, or developing new drug compounds. There is less evidence, however, on the potential of AI as a mechanism to manage human workers who perform such research tasks. We explore algorithmic management in the context of projects that push the limits of human managers’ capabilities: Crowd science projects that involve large numbers of individuals that vary greatly with respect to factors such as their skills, interests, as well as geographic location. Our analysis of archival data as well as interviews with project organizers and crowd members demonstrates the use of AI for five management functions: task division and task allocation, direction, coordination, motivation, and supporting learning. We also provide a deeper understanding of the underlying capabilities that enable AI to perform these functions, of challenges when using algorithmic management in science, and of contingency factors that may shape the effectiveness of AI. We conclude with a discussion of opportunities for future research on the organization of science and on algorithmic management.
Ciudad Politécnica de la Innovación
Edificio 8E, Acceso J, Planta 4ª (Salón Descubre. Cubo Rojo)
Universidad Politécnica de Valencia | Camino de Vera s/n
Henry joined ESMT Berlin in May 2017. He is the first holder of the ESMT Chair in Entrepreneurship. Since January 2018 Henry has been the Director of the Institute for Endowment Management and Entrepreneurial Finance (IFEE). Previously he was an associate professor of strategy and innovation and the PhD Coordinator at the Scheller College of Business at the Georgia Institute of Technology.
Henry explores the role of human capital in science, innovation, and entrepreneurship. Among others, he studies how scientists’ motives and incentives relate to important outcomes such as innovative performance in firms, patenting in academia, or career choices and entrepreneurial interests. In new projects, Henry studies the dynamics of motives and incentives over time, and explores non-traditional innovative institutions such as Crowdsourcing and Citizen Science. Additional work is underway to gain deeper insights into scientific labor markets and to derive implications for junior scientists, firms, and policy makers.