Epistemology and Theory of Machine Learning

This is the second edition of the Epistemology and Theory of Machine Learning series started in 2023.

The rapid rise and huge impact of methods in machine learning raises important philosophical questions. There is, in particular, an increasing interest in questions of epistemology: how exactly do machine learning methods contribute to the pursuit of knowledge? Issues under this header include the justification and the fundamental limitations of such methods, their interpretability, and their implications for scientific reasoning in general. Since machine learning algorithms are, in the end, formal procedures, a formally-minded philosophical approach promises to be particularly fruitful for making progress on these issues. Such a study of modern machine learning algorithms can draw from a long tradition of work in formal epistemology and philosophy of science, as well as from work in computer science and the mathematics of machine learning. The aim of this workshop is to discuss epistemological questions of machine learning in this spirit.

This edition is organized by the Emmy Noether junior research group “From Bias to Knowledge: The Epistemology of Machine Learning”, funded by the German Research Foundation (DFG) and affiliated with MCML.