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 including 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ML系统需要围绕如何设计代表性数据集进行重要考量。然而，当前很少有面向初学者的ML建模工具致力于培养数据集设计实践的动手操作能力，包括如何针对数据多样性进行设计以及如何检查数据质量。为此，我们提出了一套用于设计包容性ML模型的四项数据设计实践（DDPs），并详细介绍了如何设计一款名为Co-ML的平板端应用程序，旨在通过协作式ML模型构建体验促进用户对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——数据在多个设备间同步，使用户能够在讨论与协作中迭代优化ML数据集。我们在为期两周的AIML教育夏令营中部署了Co-ML，13至18岁的青少年以小组形式构建定制化的ML驱动移动应用。分析表明，在夏令营中学生自主项目中，多用户通过Co-ML进行模型构建的过程支持了DDPs的发展，包括纳入数据多样性、评估模型性能以及检查数据质量。此外，我们发现学生改进模型性能的尝试往往优先考虑可学习性而非类别平衡。通过本研究，我们揭示了协作机制、模型测试界面及学生驱动项目的结合如何赋能学习者主动探索数据在ML系统中的作用。