Dr Aurelia Sauerbrei
Contact information
Research groups
Aurelia Sauerbrei
Researcher, UKRI AI Metascience Fellow
Aurelia Sauerbrei is a researcher at the Ethox Centre, Nuffield Department of Population Health, and a Junior Research Fellow at Worcester College, University of Oxford. She is also a UKRI AI Metascience Fellow. Her research lies at the intersection of Ethics, Artificial Intelligence (AI), and Population Health, exploring how emerging technologies are transforming both the practice of biomedical research and medical practice, and the moral frameworks that guide them.
Research
Aurelia’s current work as a UKRI Metascience Fellow explores how AI technologies are transforming research data, and how these changes shape the ways knowledge is produced and used in scientific research. Her broader research investigates the ethical implications of using AI in healthcare, focusing on how the pursuit of efficiency within healthcare systems may come into tension with relational values such as empathy, trust, and shared decision-making. Her doctoral research at Oxford explored these tensions in depth, providing a framework for understanding how AI-driven systems can be integrated into healthcare in ethically responsible ways.
Teaching
Aurelia teaches regularly on the ethics module for the Centre for Doctoral Training in Health Data Science in Oxford’s Department of Computer Science. She also teaches AI Ethics and Medical Ethics to undergraduate students.
Recent publications
-
The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions.
Journal article
Sauerbrei A. et al, (2023), BMC Med Inform Decis Mak, 23
-
"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.
Journal article
Hallowell N. et al, (2022), BMC Med Ethics, 23
-
AIgorithmic Ethics: A Technically Sweet Solution to a Non-Problem.
Journal article
Sauerbrei A. et al, (2022), Am J Bioeth, 22, 28 - 30

