Epistemology and Theory of Machine Learning

Idea and Motivation

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 facilitate or generate 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 foundational issues of machine learning in this formal spirit.
This workshsop marks the conclusion of the project “The Epistemology of Statistical Learning Theory,” funded by the German Research Foundation (DFG).

Further Information

Website of the conference "Epistemology and Theory of Machine Learning"