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：近年来，机器学习取得了巨大进步，成为各应用领域众多突破的根源。然而，如何使其适用于高风险或安全攸关的应用领域仍是一个开放性问题，因为机器学习模型往往脆弱且不可靠。本文主张，需求定义与满足能够极大地提升机器学习模型对真实世界的适应性，尤其是在关键领域中。为此，我们提出了两个问题：（i）需求自然产生；（ii）机器学习模型已或有望得到有效部署；（iii）忽视需求可能带来灾难性后果。我们展示了如何将需求规范有效整合到标准机器学习开发流程中，并提出一种新颖的金字塔式开发过程，其中需求定义可能影响流程中的所有后续阶段，反之亦然。