Work

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

Year: 2024

Type: article

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.

Source: Information Systems Frontiers

DOI: 10.1007/s10796-024-10526-6

Authors: Daniel Leuthe, Tim Meyer-Hollatz, Tobias Plank, Anja Senkmüller

Institutions: Technische Hochschule Augsburg, University of Augsburg, Fraunhofer Institute for Applied Information Technology, University of Bayreuth, Technical University of Munich

Language: English

Topic: Green IT and Sustainability

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,090

Funder: Fraunhofer-Gesellschaft