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（ML）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，目前很少有面向初学者的ML建模工具旨在培养数据集设计实践的动手学习能力，包括如何设计数据多样性以及检查数据质量。为此，我们概述了一套用于设计包容性ML模型的四种数据设计实践（DDPs），并分享了如何设计名为Co-ML的平板端应用程序，通过协作式ML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，允许多个用户在与同伴讨论和协调中迭代优化ML数据集。我们在为期两周的教育性AIML夏令营中部署了Co-ML，13-18岁青少年以小组形式构建自定义ML驱动的移动应用。分析表明：在夏令营期间学生自主项目的背景下，通过Co-ML进行多用户模型构建，有效支持了数据多样性整合、模型性能评估和数据质量检查等DDPs的发展。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，我们强调协作、模型测试界面与学生自主项目的结合，能够赋能学习者主动探索数据在ML系统中的作用。