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Interpretable deep-learning models to help achieve the Sustainable Development Goals

Nature Machine Intelligence volume 3, page 926 (2021)Cite this article

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To the Editor — Artificial intelligence (AI) algorithms have the potential to support the Sustainable Development Goals (SDGs) of the United Nations (UN)1. For instance, Jean et al.2 propose a pioneering method to identify and track regions of poverty using satellite images via convolutional neural networks (CNNs), a study that has important implications for SDG 1 (on ending poverty in all its forms everywhere). However, one of the main limitations of current AI technology is the lack of interpretability of these models, which gives rise to concerns about trust and ethical use. In particular, decisions made by black-box AI models may not be accepted by governments or the general public, an issue that is being addressed by the ethical guidelines for ‘trustworthy AI’ by the European Commission3. We argue that there is also an excessive trust in the models, which results in decisions that cannot be explained or confirmed. An undesirable result is that officials are removed from the accountability they need to have. One well-known example of a problematic lack of interpretability is the medical application Stream developed by DeepMind, which was aimed at kidney-disease prediction4. As the deep-learning version of Stream was unable to provide interpretability of the decision-making process, it was not approved to be used in practice. Later on, the application got approval when a simple decision-tree model (which did not provide the same high accuracy as the deep-learning model) was used instead, as it provided high interpretability of the decision-making process.

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Acknowledgements

The authors acknowledge V. Dignum for her insightful comments on this manuscript. R.V. acknowledges the financial support from the Swedish Research Council (VR).

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Authors and Affiliations

  1. FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Stockholm, Sweden

    Ricardo Vinuesa

  2. AI Sustainability Center, Stockholm, Sweden

    Ricardo Vinuesa

  3. Smart Cities, School of Creative Technology, Saxion University of Applied Sciences, Enschede, The Netherlands

    Beril Sirmacek

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  1. Ricardo Vinuesa
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  2. Beril Sirmacek
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Correspondence to Ricardo Vinuesa.

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Vinuesa, R., Sirmacek, B. Interpretable deep-learning models to help achieve the Sustainable Development Goals. Nat Mach Intell 3, 926 (2021). https://doi.org/10.1038/s42256-021-00414-y

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