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A central problem for machine learning (ML) models is that they are “black boxes” and epistemically opaque. This means the inner workings of these models—how the model internally represents the data to reach a certain decision—are opaque or a “black box” to experts. This is concerning in...

Philosophical concern with epistemological challenges presented by opacity in deep neural networks does not align with the recent boom in optimism for AI in science and recent scientific breakthroughs driven by AI methods. I argue that the disconnect between philosophical pessimism and scientific op...

Machine learning (ML) and Deep learning (DL) modeling applications in science are becoming increasingly common. Despite their growing pervasiveness in the sciences, the potential implications of these models for philosophy of science have just scratched the surface. So far interest has largely cente...

The volume and variety of data in astrophysics creates a need for efficient heuristics to automate the discovery of novel phenomena. Moreover, data-driven practices suggest a role for machine-led exploration in conceptual development. I argue that philosophical accounts of exploratory experiments sh...

I draw attention to an under-theorized problem for the application of machine learning models in science, which I call semantic opacity. Semantic opacity occurs when the knowledge needed to translate the output of an unsupervised model into scientific concepts depends on theoretical assumptions abou...

One way machine learning (ML) modeling is different from more traditional modeling methods is that they are data-driven, instead of what Knüsel and Baumberger (2020) call process driven. Moreover, ML models suffer from a higher degree of model opacity compared to more traditional modeling methods. ...

AI systems are being used for a rapidly increasing number of important decisions. Many of these systems are “black boxes”: their functioning is opaque both to the people affected by them and to those developing them. This opacity is often due to the complexity of the model used by the AI system,...

In this symposium, we use the history of philosophy to illuminate specific, grounded aspects of contemporary practice in machine learning, raising new problems and proposing new frameworks for the philosophy of machine learning. Our symposium draws on 200 years of empiricism and its critics, ranging...

Abstract: Many scientific fields now benefit from ‘Big Data.’ Yet along with large datasets come an abundance of computational and statistical techniques to analyze them. Many of these techniques have not been subject to sustained philosophical scrutiny. This is in part because the scant literat...

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 us...

This paper argues that there are two different types of causes that we can wish to understand when we talk about wanting machine learning models to be explainable. The first are causes in the features that a model uses to make its predictions. The second are causes in the world that have enabled tho...