Current Research Themes
AI in Contemporary and Future Science
AI techniques are increasingly used in science, with striking and remarkable results. Yet philosophers of science are only beginning to grapple with this development as it pertains to contemporary science and its ramifications for future science.
Our work brings together philosophers of science and working scientists to address some of these issues. This work focuses on the ways AI techniques are used in various scientific subdisciplines, including radio astronomy and gravitational wave astrophysics, and the resultant challenges for future science in these areas.
Specific topics of investigation include supervised vs unsupervised AI techniques for scientific discovery, how AI is being used to complement and supplant citizen scientists, and parallels between ML and traditional modelling techniques.
Interdisciplinarity and Peer Review
"Publish or perish" is a familiar requirement in contemporary science. Since funding is necessary for most science projects, "funding or perish" is also a requirement for many professional scientists, and a lot of funding must be obtained by peer reviewed applications. While peer review is important, nobody denies that it is imperfect. Thus, there are many proposals on how to improve various features of the process. New technologies and methods, such as AI systems and alternative funding mechanisms are controversial issues in science studies.
However, we currently lack any sort of theoretical framework for systematically evaluating reform proposals. Without such frameworks, it is very easy for reformers and their critics to talk past each other, or for reformers to neglect important considerations.
We are looking at different criteria that might be included in such a framework, and applying them to the evaluation of reforms of peer review. To guide our work, we are also interviewing and working with scientists. Ultimately, we aim to assist and enrich the debates about peer review.