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.
翻译:机器学习界面(ML)、模型或数据的信息和可视化,可以帮助从业者建立强有力和负责任的ML系统。尽管这些系统有其益处,但最近对ML小组的研究和我们与从业者(n=9)的访谈表明,ML接口在实践中的采用有限。虽然现有的ML接口对具体任务有效,但设计这些接口时并没有被再利用、探索,也没有被跨功能小组中多个利益攸关方共享。为了能够进行不同从业者之间的分析和沟通,我们设计并实施了交响乐,这是一个将交互式ML接口与具体任务、数据驱动的组件组成成一个框架,可以在计算笔记本和网络仪等平台上使用。我们通过与10个小组(n=31)的参与性设计会议开发交响乐,并讨论我们在苹果公司将交响调到3个生产ML项目中的结果。交响有助于ML从业人员发现以前不知道的问题,如数据重复和模型中的盲点,同时使他们能够与其他利益攸关者分享洞察。