论文标题
为机器学习的长尾巴设计
Designing for the Long Tail of Machine Learning
论文作者
论文摘要
最近的技术进步使机器学习（ML）成为最终用户面对系统中的有希望的组成部分。但是，用户体验（UX）从业人员将ML与现有以用户为中心的设计过程以及如何导航该设计空间的可能性和约束有关。利用我们自己的经验，我们将该空间内的设计表征为在数据收集，建模开发和设计有价值的互动之间为给定的模型性能提供权衡。我们建议，关于机器学习绩效如何使用培训数据缩放的理论描述可以指导设计人员在这些权衡中，并对原型制作产生影响。我们通过争辩说，有用的模式是在自举阶段设计一个初始系统，以实现学习曲线的用法，该模式旨在利用以增加数量级的数量级收集的数据的训练效果。
Recent technical advances has made machine learning (ML) a promising component to include in end user facing systems. However, user experience (UX) practitioners face challenges in relating ML to existing user-centered design processes and how to navigate the possibilities and constraints of this design space. Drawing on our own experience, we characterize designing within this space as navigating trade-offs between data gathering, model development and designing valuable interactions for a given model performance. We suggest that the theoretical description of how machine learning performance scales with training data can guide designers in these trade-offs as well as having implications for prototyping. We exemplify the learning curve's usage by arguing that a useful pattern is to design an initial system in a bootstrap phase that aims to exploit the training effect of data collected at increasing orders of magnitude.
