AI 中文总结
该研究探讨医学与机器学习的生成类比，借鉴临床转化的认知与方法论正当性，提出一种新的ML可靠论，以解决ML在医学应用中的不确定性问题。
AI 中文摘要
在过去几年中，机器学习（ML）已在医学领域得到广泛应用，且在一定程度上取得了成功。然而，围绕ML的不确定性使得难以确立其认知与方法论正当性的基础。文献中已将医学与ML进行类比，建议我们应基于临床转化的标准来构建ML的认知与方法论标准。通过运用Hesse研究中的工具，我们将这种类比的本质描述为临床转化过程与构建ML系统过程之间的生成类比。我们更精确地确定了临床转化的认知与方法论正当性，这些正当性通常仅在诉诸类比时被提及，并展示了这些正当性在何种意义上可类比地适用于ML语境。特别地，我们以可靠论术语解读临床转化的正当性，并展示这如何能为一种新形式的ML可靠论提供信息，该理论与人工智能哲学中现有的可靠论解释不同（尽管相互兼容）。
英文摘要
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. 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. 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. 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.
CommentsAccepted for publication in Studies in the History and Philosophy of Science (cite published version)
