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Towards Sustainability of AI – Identifying Design Patterns for Sustainable Machine Learning Development

Author

Listed:
  • Daniel Leuthe

    (FIM Research Center for Information Management
    University of Applied Sciences Augsburg
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Tim Meyer-Hollatz

    (FIM Research Center for Information Management
    University of Bayreuth
    Branch Business & Information Systems Engineering of the Fraunhofer FIT)

  • Tobias Plank

    (FIM Research Center for Information Management
    University of Bayreuth
    Technical University of Munich)

  • Anja Senkmüller

    (University of Bayreuth
    Technical University of Munich)

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.

Suggested Citation

  • Daniel Leuthe & Tim Meyer-Hollatz & Tobias Plank & Anja Senkmüller, 2024. "Towards Sustainability of AI – Identifying Design Patterns for Sustainable Machine Learning Development," Information Systems Frontiers, Springer, vol. 26(6), pages 2103-2145, December.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:6:d:10.1007_s10796-024-10526-6
    DOI: 10.1007/s10796-024-10526-6
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