用医学临床转化标准构建更可靠的机器学习系统
What Can Artificial Intelligence Learn from Medicine? Generative Analogies and Reliable Machine Learning Systems
- 通过类比医学临床转化，构建机器学习的可靠机制
- 提出适用于ML的可靠性理论，增强系统可信度
- 适合关注AI可解释性与可信部署的研究者
近年来，机器学习（ML）在医学领域得到广泛应用，但其不确定性使建立其认识论与方法论基础变得困难。已有文献指出，应将临床转化的标准作为机器学习的参考。本文基于赫斯（Hesse）的工作，将临床转化过程与构建机器学习系统的过程视为生成性类比。我们精确界定了通常仅在类比中提及的临床转化的可靠性和方法论依据，并阐明这些依据如何在机器学习语境中类比适用。特别地，我们将临床转化的依据解释为可靠主义（reliabilist），并据此提出一种新的机器学习可靠主义，该理论虽不同于现有哲学中的可靠主义，但与其兼容。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。