Symphony：为机器学习编写交互式界面
Symphony: Composing Interactive Interfaces for Machine Learning
摘要：
机器学习（ML）的接口，关于模型或数据的信息和可视化，可以帮助从业者构建健壮和负责任的ML系统。尽管有这些好处，但最近对ML团队的研究和我们对从业者的采访（n=9）表明，ML接口在实践中的应用有限。虽然现有的ML接口对于特定任务是有效的，但它们的设计并不是为了让跨职能团队中的多个利益相关者重用、探索和共享。为了实现不同ML从业者之间的分析和通信，我们设计并实现了Symphony，这是一个框架，用于组成交互式ML界面，其中包含特定于任务的数据驱动组件，可以跨平台使用，例如计算笔记本和Web仪表板。我们通过与10个团队（n=31）的参与式设计会议开发了Symphony，并讨论了我们在Apple将Symphony部署到3个生产ML项目中的发现。Symphony帮助ML从业者发现以前未知的问题，如数据重复和模型中的盲点，同时使他们能够与其他利益相关者分享见解。
英文摘要：
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits, recent studies of ML teams and our interviews with practitioners (n=9) showed that ML interfaces have limited adoption in practice. While existing ML interfaces are effective for specific tasks, they are not designed to be reused, explored, and shared by multiple stakeholders in cross-functional teams. To enable analysis and communication between different ML practitioners, we designed and implemented Symphony, a framework for composing interactive ML interfaces with task-specific, data-driven components that can be used across platforms such as computational notebooks and web dashboards. We developed Symphony through participatory design sessions with 10 teams (n=31), and discuss our findings from deploying Symphony to 3 production ML projects at Apple. Symphony helped ML practitioners discover previously unknown issues like data duplicates and blind spots in models while enabling them to share insights with other stakeholders.
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。
