论文标题
机器学习系统的质量管理
Quality Management of Machine Learning Systems
论文作者
论文摘要
在过去的十年中，由于机器学习（ML）技术的重大进展，人工智能（AI）已成为我们日常生活的一部分。尽管RAW AI技术和互联网上面临应用程序的消费者的爆炸性增长，但其在业务应用程序中的采用仍然显着落后。对于业务/关键任务系统，仍然存在对AI应用程序可靠性和可维护性的严重关注。由于输出的统计性质，软件“缺陷”的定义不当。因此，必须重新评估许多传统质量管理技术，例如程序调试，静态代码分析，功能测试等。除了AI模型的正确性之外，许多其他新的质量属性，例如公平，鲁棒性，解释性，透明度等，在提供AI系统方面变得很重要。本文的目的是根据当前的进步介绍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。
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.
