What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems

人工智能能从医学中学到什么？生成式类比与可靠的机器学习系统

Abstract: In the past few years, machine learning (ML) has been widely (and to an extent, successfully) implemented in medicine. However, uncertainties surrounding ML have made it difficult to establish the bases of its epistemic and methodological warrants.

摘要： 在过去几年中，机器学习（ML）已在医学领域得到广泛（且在一定程度上成功）的应用。然而，围绕机器学习的不确定性使得建立其认识论和方法论依据变得十分困难。

In the literature, a parallel has been drawn between medicine and ML, suggesting that we should model epistemic and methodological standards for ML on the standards of clinical translation. By developing tools from Hesse work, we characterise the nature of this parallel as a generative analogy between the process of clinical translation and the process of building ML systems.

在相关文献中，医学与机器学习之间存在一种类比，即建议我们应以临床转化的标准为蓝本，构建机器学习的认识论和方法论标准。通过利用赫塞（Hesse）的研究工具，我们将这种类比的本质定义为临床转化过程与构建机器学习系统过程之间的“生成式类比”。

We identify more precisely the epistemic and methodological warrants of clinical translation that are typically only mentioned when appealing to the analogy, and we show in which sense such warrants apply analogically to the context of ML.

我们更精确地识别了临床转化中那些通常仅在引用类比时才被提及的认识论和方法论依据，并展示了这些依据在何种意义上可以类比地应用于机器学习的语境中。

In particular, we interpret warrants of clinical translation in reliabilist terms, and we show how this can inform a new form of ML reliabilism, which is distinct from (though compatible with) existing reliabilist accounts in philosophy of AI.

特别地，我们从“可靠性主义”（reliabilist）的角度解读了临床转化的依据，并展示了这如何为一种新型的“机器学习可靠性主义”提供参考。这种观点与人工智能哲学中现有的可靠性主义论述既有区别，又相互兼容。