The emergence of advanced machine learning (ML) algorithms alongside high computing capacity and big data has revolutionized how we think and act on global sustainability challenges across environmental, social, and economic dimensions. However, we lack a holistic perspective that would embrace the sociotechnical nature of ML-based solutions and their potential to contribute to or hinder sustainability throughout their full lifecycle. With this ongoing research, we aim to bring lifecycle thinking into focus when investigating the sustainability of ML-based solutions. Our preliminary findings are based on an in-depth study of Finland’s Artificial Intelligence Accelerator (FAIA) and follow-up semi-structured interviews with experts from multiple international organizations outside FAIA. The results indicate that sustainability effects, both positive and negative, are interwoven with different components of ML-based solution lifecycle. These insights extend and complement the emerging information systems literature on sustainability opportunities and threats brought by organizational use of ML technologies.
Mucha, Tomasz Marcin; Ma, Sijia; and Abhari, Kaveh, "Sustainability of Machine Learning-based Solutions: A Lifecycle Perspective" (2022). PACIS 2022 Proceedings. 262.
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