Data, Knowledge and Artificial Intelligence

Strand leader: Stephan Guttinger & Sabina Leonelli

We are concerned with the ways in which data, data infrastructures, and data-intensive technologies shape–and are shaped by–contemporary science and society. Working in dialogue with scientists, policymakers, and citizens, researchers on this theme analyse the ways in which data, samples, models, software, and hardware are used and circulated in various contexts. A particular focus is on the impacts that Open Science, citizen science, the automation and digitalisation of research, and the development of ever-more powerful AI systems have on science, and on society more generally.

Research questions pursued include:

A philosophy of Open Science: What does Open Science mean for biological and biomedical research around the globe? What are the positive and negative impacts of open practices and infrastructures, and how do biological norms and institutions need to change to accommodate them?
Data and data infrastructures in biology and biomedicine: What counts as data in these domains, and with which implications for knowledge generation? How are data infrastructures developed, maintained, and updated to support data use?
The epistemology and ethics of AI: What are the ethical challenges and governance issues raised by using AI systems to make recommendations and to support human decision-making?
Citizen science, data, and social justice: How have citizen-led forensic practices emerged in Latin America and how are they challenging and re-shaping established forensic science? How can collective forms of data-gathering, -sharing, and -verification improve data justice?
Research and data quality: How is the quality of data, data production methods and data dissemination assessed? How to make sense of reproducibility requirements across diverse research domains and conditions?
The epistemology of automated research: How does the ongoing automation of research and the push for more AI-driven experimentation change the epistemic potential and limitations of the experimental life sciences?
Data in the historical sciences: How is data generated and mustered in support of hypotheses concerning the deep past? What strategies are scientists using to deal with challenges raised by thin, degraded and gappy data?