From Bias to Knowledge: The Epistemology of Machine Learning (2023 - 2029)

Abstract

There are various biases entering in the development of a machine learning system. Relatively unexplored so far is the concept of inductive bias, which lies at the very heart of machine learning: inductive biases are the assumptions that allow a learning algorithm to learn. This project connects the philosophy of science and the mathematical theory of machine learning to clarify the concept of inductive bias. This better understanding of inductive bias will be the central element in a general epistemological account of how we gain knowledge through machine learning methods.

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