As artificial intelligence (AI) and machine learning (ML) advance, concerns about their sustainability impact grow. The emerging field "Sustainability of AI" addresses this issue, with papers exploring distinct aspects of ML’s sustainability. However, it lacks a comprehensive approach that considers all ML development phases, treats sustainability holistically, and incorporates practitioner feedback. In response, we developed the sustainable ML design pattern matrix (SML-DPM) consisting of 35 design patterns grounded in justificatory knowledge from research, refined with naturalistic insights from expert interviews and validated in three real-world case studies using a web-based instantiation. The design patterns are structured along a four-phased ML development process, the sustainability dimensions of environmental, social, and governance (ESG), and allocated to five ML stakeholder groups. It represents the first artifact to enhance each ML development phase along each ESG dimension. The SML-DPM fuels advancement by aggregating distinct research, laying the groundwork for future investigations, and providing a roadmap for sustainable ML development.

随着人工智能 (AI) 和机器学习 (ML) 的进步，人们对其可持续性影响的担忧与日俱增。新兴领域“人工智能的可持续性”解决了这个问题，其论文探讨了机器学习可持续性的不同方面。然而，它缺乏一种全面的方法来考虑所有机器学习开发阶段、整体对待可持续性并纳入从业者的反馈。为此，我们开发了可持续的机器学习设计模式矩阵 (SML-DPM)，其中包含 35 种设计模式，这些设计模式以研究中的合理知识为基础，根据专家访谈中的自然见解进行提炼，并使用基于网络的实例化在三个现实案例研究中进行了验证。设计模式按照四个阶段的机器学习开发流程、环境、社会和治理 (ESG) 的可持续性维度构建，并分配给五个机器学习利益相关者群体。它代表了沿着每个 ESG 维度增强每个 ML 开发阶段的第一个工件。 SML-DPM 通过聚合不同的研究、为未来的研究奠定基础并为可持续的 ML 开发提供路线图来推动进步。