Machine learning (ML) models are fundamentally shaped by data, and building inclusive ML systems requires significant considerations around how to design representative datasets. Yet, few novice-oriented ML modeling tools are designed to foster hands-on learning of dataset design practices, including how to design for data diversity and inspect for data quality. To this end, we outline a set of four data design practices (DDPs) for designing inclusive ML models and share how we designed a tablet-based application called Co-ML to foster learning of DDPs through a collaborative ML model building experience. With Co-ML, beginners can build image classifiers through a distributed experience where data is synchronized across multiple devices, enabling multiple users to iteratively refine ML datasets in discussion and coordination with their peers. We deployed Co-ML in a 2-week-long educational AIML Summer Camp, where youth ages 13-18 worked in groups to build custom ML-powered mobile applications. Our analysis reveals how multi-user model building with Co-ML, in the context of student-driven projects created during the summer camp, supported development of DDPs involving incorporating data diversity, evaluating model performance, and inspecting for data quality. Additionally, we found that students' attempts to improve model performance often prioritized learnability over class balance. Through this work, we highlight how the combination of collaboration, model testing interfaces, and student-driven projects can empower learners to actively engage in exploring the role of data in ML systems.

翻译：摘要：机器学习模型从根本上由数据塑造，构建包容性的机器学习系统需要深入思考如何设计具有代表性的数据集。然而，现有面向初学者的机器学习建模工具中，鲜有专门设计用于促进数据集设计实践（如数据多样性设计与数据质量检查）的动手学习。为此，我们提出一套由四项数据设计实践（DDPs）构成的框架，用于指导包容性机器学习模型的设计，并展示如何开发一款名为Co-ML的平板端应用程序，通过协作式机器学习模型构建体验促进DDPs的学习。借助Co-ML，初学者可通过分布式体验构建图像分类器——数据在多设备间同步，支持多名用户在与同伴讨论和协调中迭代优化机器学习数据集。我们在为期两周的AI/ML教育夏令营中部署了Co-ML，其中13-18岁的青少年以小组形式构建了基于机器学习的定制化移动应用程序。分析表明，在夏令营中学生驱动型项目的背景下，通过Co-ML进行多用户模型构建有助于培养以下DDPs：整合数据多样性、评估模型性能、检查数据质量。此外，我们发现学生在尝试提升模型性能时，往往将可学习性置于类别平衡之前。本研究强调了协作机制、模型测试界面与学生驱动型项目的结合，能够赋能学习者主动探索数据在机器学习系统中的作用。