In the past decade, Artificial Intelligence (AI) has become a part of our daily lives due to major advances in Machine Learning (ML) techniques. In spite of an explosive growth in the raw AI technology and in consumer facing applications on the internet, its adoption in business applications has conspicuously lagged behind. For business/mission-critical systems, serious concerns about reliability and maintainability of AI applications remain. Due to the statistical nature of the output, software 'defects' are not well defined. Consequently, many traditional quality management techniques such as program debugging, static code analysis, functional testing, etc. have to be reevaluated. Beyond the correctness of an AI model, many other new quality attributes, such as fairness, robustness, explainability, transparency, etc. become important in delivering an AI system. The purpose of this paper is to present a view of a holistic quality management framework for ML applications based on the current advances and identify new areas of software engineering research to achieve a more trustworthy AI.

中文翻译：

---

机器学习系统的质量管理

在过去十年中，由于机器学习 (ML) 技术的重大进步，人工智能 (AI) 已成为我们日常生活的一部分。尽管原始人工智能技术和互联网上面向消费者的应用程序呈爆炸式增长，但它在商业应用程序中的采用却明显滞后。对于业务/任务关键型系统，仍然存在对 AI 应用程序可靠性和可维护性的严重担忧。由于输出的统计性质，软件“缺陷”没有明确定义。因此，许多传统的质量管理技术，如程序调试、静态代码分析、功能测试等，必须重新评估。除了 AI 模型的正确性之外，还有许多其他新的质量属性，例如公平性、稳健性、可解释性、透明度等。在交付 AI 系统方面变得很重要。本文的目的是基于当前的进展提出一个 ML 应用程序的整体质量管理框架的观点，并确定软件工程研究的新领域，以实现更值得信赖的 AI。