  As machine learning models are increasingly used for high-stakes decision
making, scholars have sought to intervene to ensure that such models do not
encode undesirable social and political values. However, little attention thus
far has been given to how values influence the machine learning discipline as a
whole. How do values influence what the discipline focuses on and the way it
develops? If undesirable values are at play at the level of the discipline,
then intervening on particular models will not suffice to address the problem.
Instead, interventions at the disciplinary-level are required. This paper
analyzes the discipline of machine learning through the lens of philosophy of
science. We develop a conceptual framework to evaluate the process through
which types of machine learning models (e.g. neural networks, support vector
machines, graphical models) become predominant. The rise and fall of
model-types is often framed as objective progress. However, such disciplinary
shifts are more nuanced. First, we argue that the rise of a model-type is
self-reinforcing--it influences the way model-types are evaluated. For example,
the rise of deep learning was entangled with a greater focus on evaluations in
compute-rich and data-rich environments. Second, the way model-types are
evaluated encodes loaded social and political values. For example, a greater
focus on evaluations in compute-rich and data-rich environments encodes values
about centralization of power, privacy, and environmental concerns.
