Science in the Digital Era

Redressing the Balance: A Yin-Yang Perspective on Information Technology

Information is an essential aspect of how we interact with the world around us. We acquire information and then integrate it to build knowledge, understanding, and trust, which in turn serve in preparing actions. Information technology (IT) is supposed to support all these phases of information processing. But does it? An assessment of IT through the yin-yang lens from Chinese philosophy shows that over the last decades, support for the yin processes of building knowledge, understanding, and trust has been neglected, the focus of most research and development having been on the yang processes of acting. IT shares this imbalance with other aspects of Western and globalized culture. I discuss possible directions for re-establishing a yin-yang balance in IT, as a small contribution to redressing the balance in the world at large.

Improving Science That Uses Code

Abstract. As code is now an inextricable part of science it should be supported by competent Software Engineering, analogously to statistical claims being

Establishing trust in automated reasoning - MetaROR

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by early practitioners of computational science, but was later forgotten. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.

Why do we do astrophysics?

At time of writing, large language models (LLMs) are beginning to obtain the ability to design, execute, write up, and referee scientific projects on the data-science side of astrophysics. What implications does this have for our profession? In this white paper, I list - and argue for - a set of facts or "points of agreement" about what astrophysics is, or should be; these include considerations of novelty, people-centrism, trust, and (the lack of) clinical value. I then list and discuss every possible benefit that astrophysics can be seen as bringing to us, and to science, and to universities, and to the world; these include considerations of love, weaponry, and personal (and personnel) development. I conclude with a discussion of two possible (extreme and bad) policy recommendations related to the use of LLMs in astrophysics, dubbed "let-them-cook" and "ban-and-punish." I argue strongly against both of these; it is not going to be easy to develop or adopt good moderate policies.