Science has changed considerably in recent decades and continues to change at a remarkable rate. Many of the changes concern how science is done. Computer simulations are used to make predictions and to propose explanations. Gigantic databases of information—collections of ‘big data’—are analyzed for patterns, to generate and test hypotheses. New areas of interdisciplinary science are forming, which involve attempts to integrate approaches from the older natural scientific disciplines of chemistry, physics, and biology. AI is being used to perform tasks that humans simply cannot. On the horizon are AI scientists.

To what extent can experiments done on computers—computer simulations—stand in for experiments done ‘for real’, in the laboratory or the field? How can conflicts about what counts as a good method of inquiry, between scientists working in different areas, be resolved effectively? How does the changing social structure of science bear on what we should expect future science to produce? What are the advantages and disadvantages of having AI perform specific functions in future science? How might AI best be deployed?

This project combines the expertise of both philosophers and scientists. We work together to examine some of the difficulties created by these new approaches and emerging developments, and tackle some of the interesting questions that they raise about the future of science. Our research will impact major issues in the philosophy of science, such as the limits of what science can achieve given methodological and epistemic trends created by technological changes in contemporary science. We will also produce results with practical significance for science, such as suggestions for improving  interdisciplinary research collaborations and institutions.

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.