Foundational values for foundation models
2026-08-10 • Computers and Society
Computers and Society
AI summaryⓘ
The authors study how research values influence decisions in creating machine learning tools for medical images. They focus on 'foundation models,' a kind of large AI model, examining why some researchers choose to use them while others do not. By asking deep questions about these choices, the authors help explain how these models fit into the bigger picture of medical AI research.
research valuesmachine learningmedical imagingfoundation modelstechnical decisionsnormative dimensionSocratic approachphilosophy of machine learning
Authors
John S. H. Baxter, Elodie Germani
Abstract
Research values, properties with a distinctive normative dimension, often affect how technological research is performed in both direct and indirect ways by influencing how technical decisions are made. In machine learning for medical imaging, understanding these values can be important for understanding why particular researchers justify the decisions made in their publications and explain why certain technologies become ubiquitous (or not) in the scientific literature and in the clinic. This article explores one of these technologies, foundation models, finding detailed justifications both for their use and abstention from their use. By taking a Socratic approach to research values arising from this specific technical decision, this article aims to better illustrate how foundation models fit into the philosophy of machine learning in medicine.