Seminar on History and Philosophy of Science

Abstract: Funding science is a chancy business. Promising projects come to naught; strong results fail to replicate. To reduce the uncertainty of their bets, grant-making agencies are encouraging the development of tools that aim to predict which papers will replicate -- and, eventually, which grants to fund. The putative benefits of a tool that could predict the success of funding proposals are clear: time saved, public funds better allocated. But in this talk, we argue that machine learning-based predictive instruments are likely to homogenize which science is funded, with negative implications for epistemic diversity in science. We generate four possible scenarios for the deployment of algorithmic decision-making tools in grant evaluation by varying the predictive success of the average tool and the degree of correlation between the tools. We show that no matter how predictively successful the tool is, using machine learning to filter grants is likely to reduce the diversity of scientific approaches. We conclude by recommending solutions to grant-making agencies.

Written with Liam Kofi Bright.