A recent study conducted by researchers at the European School of Management and Technology (ESMT) Berlin sheds light on the potential role of artificial intelligence (AI) in managing human participants in scientific research endeavors.
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How AI Could Assume Managerial Roles in Scientific Studies
Led by Maximilian Koehler, a Ph.D. candidate at ESMT, and Henry Sauermann, a professor of strategy at the same institution, the study explores how AI could assume managerial functions in large-scale research projects, including task allocation, coordination, and motivation.
Rather than functioning as mere workers executing specific research tasks, AI in this context is envisioned as a manager overseeing human workers engaged in tasks such as data collection and analysis.
Referred to as algorithmic management (AM), this concept represents a significant departure from traditional research project management practices and holds the promise of enhancing project scalability and efficiency, according to the research team.
The study delves into the burgeoning field of algorithmic management in crowd and citizen science projects. Koehler and Sauermann present examples demonstrating how AI can effectively perform key managerial functions, including task division and allocation, direction, coordination, motivation, and supporting learning.
Drawing on a variety of sources, including online documentation and interviews with project organizers, AI developers, and participants, the researchers identify instances where algorithmic management is employed, examine how AI executes managerial functions, and explore the conditions under which AM proves most effective.
AI's Role in Scientific Research
According to Koehler, the capabilities of AI have advanced to a point where it can significantly augment the scope and efficiency of scientific research endeavors by assuming managerial responsibilities in complex, large-scale projects.
Quantitative analysis conducted as part of the study indicates that AM-enabled projects tend to be larger in scale compared to those that do not utilize AM. Moreover, such projects are often associated with platforms that provide access to shared AI tools, suggesting that the adoption of AM necessitates robust technical infrastructures that standalone projects may struggle to develop.
These findings have implications for various stakeholders in the research ecosystem, including research funders, digital research platforms, and larger research organizations.
While AI may assume critical management functions, Sauermann emphasizes that this does not render principal investigators or human managers obsolete. Instead, he suggests that human leaders could redirect their focus towards more strategic and social tasks, such as identifying research priorities, securing funding, or fostering organizational culture.
"If AI can take over some of the more algorithmic and mundane functions of management, human leaders could shift their attention to more strategic and social tasks such as identifying high-value research targets, raising funding, or building an effective organizational culture," Sauermann said in a press release statement.
The study, titled "Algorithmic management in scientific research," was published in the journal Research Policy.
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