Abstract
Over the past decade, scientific researchers and applied data scientists have steadily adopted machine learning (ML) techniques, particularly highly parameterized deep neural networks, Deep Learning (DL). Trained estimators resulting from such ML processes, referred to as models, are now commonly used to either better estimate particular unknown features given a particular context or to improve understanding of said features given their respective contexts. Recently philosophical work has investigated the nature of such understanding from ML models. Sullivan (2020) argues that the complexity of DL trained models means that they can be contrasted with the traditional use of idealization models, which ostensibly enable explanation or understanding by reducing complexity. In this work we explore the strength of this contrast, arguing that while the explicit functional form of particular highly parameterized DL trained models can be quite complex, such complexities are irrelevant to gains in explanation or understanding generated by DL models. We observe that framing the form of understanding gained from ML models as in Tamir & Shech (2022) enables an account of understanding from ML models that consequently illuminates both the nuances and failures of this contrast. Specifically, we propose that individual parameter instantiations resulting from ML training of particular models are best understood as approximations of the more general target phenomenon to be understood. We demonstrate that a proper analysis in which the contexts where approximation relationships break down are distinguished from those in which it can be sustained, enables us identify both sort of details irrelevant to understanding and the sort of higher level representations often captured by hidden layers of deep neural networks which may be leveraged for explanation or improved understanding. We show that hindrances to understanding from ML models due to parametrization complexity are analogous to infinite idealization dilemmas found in the philosophy of physics literature (Batterman 2002, Shech 2013). Drawing on Norton’s (2012) distinction between idealizations and approximations, we argue that our resolution of understanding from ML models despite parameterization complexity has important parallels with resolutions of said infinite idealization dilemmas, viz., Butterfield (2011), Norton (2012). We conclude with a unifying framework under which the success of accounts of understanding from highly parameterized ML models as well as understanding from some idealized models (including problematic infinite models) can be properly assessed.
