In the recent years, machine learning has made great advancements that have been at the root of many breakthroughs in different application domains. However, it is still an open issue how make them applicable to high-stakes or safety-critical application domains, as they can often be brittle and unreliable. In this paper, we argue that requirements definition and satisfaction can go a long way to make machine learning models even more fitting to the real world, especially in critical domains. To this end, we present two problems in which (i) requirements arise naturally, (ii) machine learning models are or can be fruitfully deployed, and (iii) neglecting the requirements can have dramatic consequences. We show how the requirements specification can be fruitfully integrated into the standard machine learning development pipeline, proposing a novel pyramid development process in which requirements definition may impact all the subsequent phases in the pipeline, and viceversa.
翻译:在最近的几年里,机器学习取得了重大进展,这些进展是许多不同应用领域突破的根源。然而,如何使它们适用于高风险或安全关键的应用领域仍然是一个开放问题,因为它们可能经常很脆弱、不可靠。在本文中,我们认为需求定义和满足可以大有作为,使机器学习模型在真实环境中更加适用,特别是在关键领域中。为此,我们提出了两个问题,在这两个问题中,(i)需求自然产生,(ii)机器学习模型正在或可以成功部署,并且(iii)忽略需求可能会产生重大后果。我们展示了如何将需求规范成功地集成到标准机器学习开发流程中,提出了一种新的金字塔样式的开发过程,在这种开发过程中,需求定义可能会影响到随后所有的阶段,反之亦然。