2022/05/03 by Oliver Buchholz, Eric Raidl, Éric Raidl · 3 citations
Computer Science · Mathematics · Social Sciences · #Adversarial Robustness in Machine Learning #Algorithmic learning theory #Artificial intelligence #Artificial neural network #Computer science #Empirical risk minimization #Ethics and Social Impacts of AI #Explainable Artificial Intelligence (XAI) #Machine learning #Online machine learning #Regularization (linguistics) #Unsupervised learning #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.1086/721797
published in The British Journal for the Philosophy of Science 76(4), 1011-1035 (Oxford University Press) · 27 pages, 2 figures
arxiv created 2022/05/03 · openalex created_date 2022/05/08 · openalex publication_date 2022/07/22 · arxiv updated 2022/08/10 · openalex updated_date 2026/08/05
Machine learning operates at the intersection of statistics and computer science. This raises the question as to its underlying methodology. While much emphasis has been put on the close link between the process of learning from data and induction, the falsificationist component of machine learning has received minor attention. In this paper, we argue that the idea of falsification is central to the methodology of machine learning. It is commonly thought that machine learning algorithms infer general prediction rules from past observations. This is akin to a statistical procedure by which estimates are obtained from a sample of data. But machine learning algorithms can also be described as choosing one prediction rule from an entire class of functions. In particular, the algorithm that determines the weights of an artificial neural network operates by empirical risk minimization and rejects prediction rules that lack empirical adequacy. It also exhibits a behavior of implicit regularization that pushes hypothesis choice toward simpler prediction rules. We argue that taking both aspects together gives rise to a falsificationist account of artificial neural networks.