Collegium Helveticum
Switzerland
fellow
Bjørn Hofmann
2024-2025
Home institution
University of Oslo and Norwegian University of Science and Technology at Gjøvik,
Country of origin (home institution)
Norway
Discipline(s)
Anthropology and ethnology; Computers and intelligent systems; Philosophy
Theme(s)
Artificial Intelligence; Digital Society
Fellowship dates
›
Biography
Bjørn Hofmann has a special interest for the relationship between epistemology and ethics. He is affiliated with and the Centre for Medical Ethics at the University of Oslo in Norway and the Department of Health Science at the Norwegian University of Science and Technology (NTNU) at Gjøvik. Bjørn's academic background spans natural sciences, the history of ideas, and philosophy. His primary research interests include fundamental concepts for health care, norms of knowledge and evidence production, the theory and practice of governing technology, health services research, and (bio)medical ethics.
Research Project
The Ethics of Biases of Artificial Intelligence and Machine Learning in Medicine
This project explores the ethical dimensions of biases in artificial intelligence and machine learning (AI/ML) in medicine and healthcare, a critical issue given the rapid expansion of these technologies. It investigates the sources of algorithmic bias, including factors such as biased training data, skewed feature selection, and modeling choices.
The project delves into how biases related to the implementation and use of AI/ML technologies shape outcomes, revealing a complex web of challenges. Building on this, it explores the ethical implications of these biases and seeks to determine whether existing ethical frameworks are sufficient or if new principles are needed. By examining the intersection of epistemology and ethics, the project aims to shed light on how we understand and address the biases embedded within AI/ML systems.
To ground its inquiry, the project focuses on a compelling case study: the use of AI/ML in breast cancer screening and detection. This field provides a particularly rich context, as AI/ML has been widely adopted here, yet biased training and validation data remain persistent issues, often driven by screening overdiagnosis and overtreatment. At the same time, rigorous methods have been introduced to improve the diagnostic sensitivity of mammograms, offering a unique lens through which to evaluate the ethical and epistemological challenges posed by bias in AI/ML.
Research Interests:
AI ethics; algorithmic bias; machine learning; healthcare AI; medical ethics; epistemology; training data; feature selection; breast cancer screening; medical imaging; diagnostic medicine.