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，机器学习取得了长足进步，这已成为不同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关键型应用领域仍是一个未解难题，因为这些模型往往脆弱且不可靠。本文论证了需求定义与满足能够显著提升机器学习模型对现实世界的适应性，尤其是在关键领域。为此，我们提出两个典型问题：(i)需求自然涌现，(ii)机器学习模型已经或可以高效部署，(iii)忽视需求可能引发灾难性后果。我们展示了如何将需求规范有效整合至标准机器学习开发流程，并提出一种新型金字塔式开发过程——其中需求定义可能影响流程中所有后续阶段，反之亦然。