The Philosophy of Science and its relation to Machine Learning

• Published in: Scientific Data Mining and Knowledge Discovery pp. 77 - 89
• Main Author: Williamson, Jon
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Subjects: Certainty Factor; Inductive Logic Programming; Inductive Logic Programming System; Judgement Aggregation; Qualitative Evidence
• ISBN: 3642027873; 9783642027871
• DOI: 10.1007/978-3-642-02788-8_4

In this chapter I discuss connections between machine learning and the philosophy of science. First I consider the relationship between the two disciplines. There is a clear analogy between hypothesis choice in science and model selection in machine learning. While this analogy has been invoked to argue that the two disciplines are essentially doing the same thing and should merge, I maintain that the disciplines are distinct but related and that there is a dynamic interaction operating between the two: a series of mutually beneficial interactions that changes over time. I will introduce some particularly fruitful interactions, in particular the consequences of automated scientific discovery for the debate on inductivism versus falsificationism in the philosophy of science, and the importance of philosophical work on Bayesian epistemology and causality for contemporary machine learning. I will close by suggesting the locus of a possible future interaction: evidence integration.