Artificial intelligence (AI) is currently based largely on black-box machine learning models
which lack interpretability. The field of eXplainable AI (XAI) strives to address this major …

Abstract Machine learning (ML) models recently led to major breakthroughs in predictive
tasks in the natural sciences. Yet their benefits for the social sciences are less evident, as …

Issues of reliability are claiming center‐stage in the epistemology of machine learning. This
paper unifies different branches in the literature and points to promising research directions …

Simple Summary The emergence of precision livestock farming (PLF) raises important
issues for many different social groups, including farmers, consumers, regulators, and the …

I argue that machine learning (ML) models used in science function as highly idealized toy
models. If we treat ML models as a type of highly idealized toy model, then we can deploy …

Deep neural networks are said to be opaque, impeding the development of safe and
trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the …

Models and modeling have played an increasingly important role in philosophy, going back
to the nineteenth century. While philosophical interest in models has been remarkably lively …

It has been argued that inductive underdetermination entails that machine learning
algorithms must be value-laden. This paper offers a more precise account of what it would …

The functions of technical artifacts are closely associated with design. Increasingly, however,
we depend on technologies that are not designed: algorithms produced using machine …

Many have recently argued that there are weighty reasons against making high-stakes
decisions solely on the basis of recommendations from artificially intelligent (AI) systems …