Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/319255 
Year of Publication: 
2024
Citation: 
[Journal:] Information Systems Frontiers [ISSN:] 1572-9419 [Volume:] 26 [Issue:] 6 [Publisher:] Springer US [Place:] New York [Year:] 2024 [Pages:] 2103-2145
Publisher: 
Springer US, New York
Abstract: 
Abstract 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.
Subjects: 
Artificial intelligence
Design patterns
ESG
Machine learning
Sustainability of AI
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version
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