定义值得信赖的AI野花监测平台的质量要求
Defining Quality Requirements for a Trustworthy AI Wildflower Monitoring Platform
摘要：
对于从经过训练的机器学习模型演变为生产就绪的AI系统的AI解决方案，需要考虑的不仅仅是机器学习模型的性能。生产就绪的AI系统需要值得信赖，即高质量。但在实践中如何确定这一点？对于传统的软件，ISO25000及其前身长期以来一直被用来定义和测量质量特性。最近，基于ISO25000的AI系统质量模型已经被引入。本文将一个这样的质量模型应用于现实生活中的案例研究：用于监测野花的深度学习平台。本文提出了三个现实场景，分别描述了使用、扩展和逐步改进用于野花识别和计数的深度学习平台的意义。接下来，它显示了如何质量模型可以被用作一个结构化的字典来定义数据，模型和软件的质量要求。未来的工作仍然是用指标、工具和最佳实践来扩展质量模型，以帮助人工智能工程从业者实施值得信赖的人工智能系统。
英文摘要：
For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
