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.

通过科学哲学的视角分析机器学习学科，开发一个概念框架以评估机器学习模型发展的过程，并探讨价值观如何影响该学科的发展和模型类型的崛起。我们认为模型类型的崛起是自我强化的，并且模型类型的评估会编码社会和政治价值观，例如围绕计算和数据密集型环境的评估方式关注权力集中、隐私和环境保护等价值观。因此，需要考虑纪律层面的干预以应对与学科整体相关的不良价值观。