Several authors have recently made a connection between algorithmic bias in machine learning and the debate about the value-free ideal in the philosophy of science. Using arguments that set out from the underdetermination of inductive conclusions by the data, a trait shared by scientific reasoning and machine learning algorithms, some have concluded that machine learning algorithms *must* be value-laden.

Drawing from the philosophy of induction, I aim to show why a general argument from underdetermination does *not* already settle that learning algorithms are value-laden. I first clarify the relevant notions of “value-ladenness” and “machine learning algorithm”. Then, relying on a distinction between domain-general learning rules and their domain-specific inductive biases, I argue that underdetermination does not yet preclude that either component is epistemically motivated or even justified. I illustrate my observations by the general framework of Bayesian induction and by the specific example of a classical convolutional neural network.