Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic, and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence, and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our article provides a comprehensive survey of the state of the art in the assurance of ML , i.e., in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle , i.e., of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The article begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research.

中文翻译：

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确保机器学习生命周期

机器学习已经发展成为一种支持技术，适用于各种非常成功的应用。这种成功持续和加速的潜力已将机器学习 (ML) 置于研究、经济和政治议程的首位。这种前所未有的兴趣是由机器学习适用性扩展到医疗保健、交通、国防和其他具有重要社会意义的领域的愿景推动的。实现这一愿景需要在安全关键型应用程序中使用机器学习，这些应用程序需要超出当前机器学习应用程序所需的保证水平。我们的文章对机器学习保证，即，在生成 ML 对于其预期用途足够安全的证据时。该调查涵盖了能够在不同阶段提供此类证据的方法机器学习生命周期，即复杂的迭代过程，从收集用于为系统训练 ML 组件的数据开始，到在系统内部署该组件结束。本文首先系统地介绍了 ML 生命周期及其阶段。然后，我们为每个阶段定义保证需求，审查有助于实现这些需求的现有方法，并确定需要进一步研究的开放挑战。