Digitalization and Opening up Science

The interdisciplinary group is dedicated to investigating the digitalization and opening up of science and is actively involved in shaping these processes. Its research focuses on data infrastructures and data tools for interdisciplinary, data-driven research. In this context, the group investigates the scientific practices surrounding the development and application of AI; in short: "AI as a science" and "science with AI."

A core focus of the group’s work involves the critical examination of the ethical dimensions of AI research, alongside the challenges posed by its expanding role as a research instrument in interdisciplinary and data-intensive domains.

Some of the group’s guiding research questions are:

• What opportunities and risks arise from the digitalization and opening up of science, in particular from the establishment of data infrastructures and data tools for AI researchers?
• How do these developments intersect with principles of transparency, reproducibility, and the ethics of AI research?
• How can research data and other digital artifacts for AI researchers best be represented and linked using modern technologies?
• How can these digital artifacts of AI research (data, models, and software) be systematically documented and linked to ensure transparency, reproducibility, and ethical accountability?
• Which data infrastructures and data tools are suitable for AI researchers? How can these data infrastructures and data tools best be improved using modern technologies?
• What conditions must be established to fortify transparency, reproducibility, and adherence to ethical principles in AI research?

The group’s overarching objective is to elucidate the sociotechnical implications of AI systems on both practices and broader societal structures, and to design research practices, data infrastructures, and data tools such that these considerations are explicitly taken into account. In particular, the focus is on establishing transparent, reproducible, and ethical development and evaluation approaches that promote a more equitable use of AI for research and society.