This paper considers the epistemic justification for a simplicity preference
in inductive inference that may be obtained from the machine learning framework
of statistical learning theory. Uniting elements from both earlier arguments
suggesting and rejecting such a justification, the paper spells out a qualified
means-ends and model-relative justificatory argument, built on statistical
learning theory's central mathematical learning guarantee for the method of
empirical risk minimization.

该论文考虑了归纳推理中由统计学习理论框架得出的简洁性偏好的认识论理解。通过结合先前论证的元素，该论文阐述了一个基于统计学习理论的、有限的目的手段和模型相关的证明论论证，建立在经验风险最小化方法的统计学习理论的中心数学学习保证的基础上。

统计学习理论与奥卡姆剃刀：基于经验风险最小化的论证

Statistical learning theory and Occam's razor: The argument from  empirical risk minimization

Philosophers have recently focused on critical, epistemological challenges
that arise from the opacity of deep neural networks. One might conclude from
this literature that doing good science with opaque models is exceptionally
challenging, if not impossible. Yet, this is hard to square with the recent
boom in optimism for AI in science alongside a flood of recent scientific
breakthroughs driven by AI methods. In this paper, I argue that the disconnect
between philosophical pessimism and scientific optimism is driven by a failure
to examine how AI is actually used in science. I show that, in order to
understand the epistemic justification for AI-powered breakthroughs,
philosophers must examine the role played by deep learning as part of a wider
process of discovery. The philosophical distinction between the 'context of
discovery' and the 'context of justification' is helpful in this regard. I
demonstrate the importance of attending to this distinction with two cases
drawn from the scientific literature, and show that epistemic opacity need not
diminish AI's capacity to lead scientists to significant and justifiable
breakthroughs.

本文讨论深度神经网络的透明度带来的批判性和认识论挑战，并探讨了 AI 在科学中的作用，阐明了深度学习在发现过程中的重要作用，并结合两个科学案例阐释了 AI 在引领科学界取得重大和可证明突破方面的作用。