机器学习系统中的错误行为与容错
On Misbehaviour and Fault Tolerance in Machine Learning Systems
摘要：
机器学习（ML）为我们提供了许多机会，使ML系统能够适应新的情况和环境。与此同时，这种适应性提高了有关运行时的产品质量或可靠性，如可靠性和安全性，这些系统的不确定性。系统可以被测试和监控，但这并不能提供对适应ML系统本身的故障和失败的保护。我们研究了旨在在ML系统中引入容错的软件设计，以便可以避免系统的ML组件中可能出现的问题。研究是作为一个案例研究，其数据收集通过五个半结构化的采访经验丰富的软件架构师。我们提出了一个概念化的ML系统的不当行为，容错的感知作用，和使用的设计。以容错方式将ML组件合并到设计中的常见模式已经开始出现。例如，ML模型通过监控输入及其分布，并在可接受的输出上执行业务规则来保护。使用多个专业的ML模型来适应周围世界的变化和变化，并采用更简单的故障技术（如默认输出）来使系统在遇到问题时启动和运行。然而，这些模式的一般作用没有得到广泛承认。这主要是由于使用ML作为完整软件系统的一部分相对不成熟：该领域仍然缺乏既定的框架和实践，而不是培训来实现，操作和维护使用ML的软件。ML软件工程需要在各个方面进行进一步的分析和开发。
英文摘要：
Machine learning (ML) provides us with numerous opportunities, allowing ML systems to adapt to new situations and contexts. At the same time, this adaptability raises uncertainties concerning the run-time product quality or dependability, such as reliability and security, of these systems. Systems can be tested and monitored, but this does not provide protection against faults and failures in adapted ML systems themselves. We studied software designs that aim at introducing fault tolerance in ML systems so that possible problems in ML components of the systems can be avoided. The research was conducted as a case study, and its data was collected through five semi-structured interviews with experienced software architects. We present a conceptualisation of the misbehaviour of ML systems, the perceived role of fault tolerance, and the designs used. Common patterns to incorporating ML components in design in a fault tolerant fashion have started to emerge. ML models are, for example, guarded by monitoring the inputs and their distribution, and enforcing business rules on acceptable outputs. Multiple, specialised ML models are used to adapt to the variations and changes in the surrounding world, and simpler fall-over techniques like default outputs are put in place to have systems up and running in the face of problems. However, the general role of these patterns is not widely acknowledged. This is mainly due to the relative immaturity of using ML as part of a complete software system: the field still lacks established frameworks and practices beyond training to implement, operate, and maintain the software that utilises ML. ML software engineering needs further analysis and development on all fronts.
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。
