Introduction

"What AI companies are doing is a bit like releasing race cars without seatbelts or fully working brakes and figuring things out as they go." Melissa Heikkilä (2023)

As artificial intelligence (AI) and its subset 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 sustainability. However, it lacks a comprehensive approach that considers all ML development phases, treats sustainability holistically, and incorporates practitioner feedback. In response, using Design Science Research, we developed the sustainable ML design pattern matrix (SML-DPM) consisting of 35 design patterns. 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. Through expert evaluation, we validated the SML-DPM's applicability and usefulness. It represents the first artifact to enhance each ML development phase along the ESG dimensions. The SML-DPM fuels advancement by aggregating distinct research, laying the groundwork for future investigations, and providing a roadmap for sustainable ML development.

The resulting matrix:

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