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Federated learning and Indigenous genomic data sovereignty

Nature Machine Intelligence volume 4, pages 909–911 (2022)Cite this article

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Indigenous peoples are under-represented in genomic datasets, which can lead to limited accuracy and utility of machine learning models in precision health. While open data sharing undermines rights of Indigenous communities to govern data decisions, federated learning may facilitate secure and community-consented data sharing.

Despite efforts to increase the inclusion of diverse populations in research studies, Indigenous peoples remain under-represented in genomics datasets1,2,3. Recent and past unethical research conduct involving the collection of biological and health data from Indigenous communities has strained relationships between Indigenous peoples and researchers4,5, often resulting in tribal policies that restrict open data sharing. This poses a difficulty in fields such as bioinformatics in which the sharing of diverse sets of digital sequence information is vital for the development of robust machine learning models that inform genomic and clinical algorithms. The exclusion of Indigenous peoples from datasets limits the predictive accuracy of machine learning models, increases the potential for unintended biases and may contribute to misinformed data decisions affecting precision healthcare of Indigenous patients. Hence, there is a sustained need to facilitate secure and community-consented Indigenous data sharing that respects the right of Indigenous communities to self-govern data decisions concerning genomic information from their own peoples, known as Indigenous genomic data sovereignty6.

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Acknowledgements

N.B. thanks J. T. Topham and G. Pistilli for their advice and feedback on the manuscript. K.S.T. and K.F. are grateful to the ENRICH (Equity for Indigenous Research and Innovation Coordinating Hub) program at New York University and University of Waikato, funded in part through a gift from the Minderoo Foundation, for their support for machine-focused solutions that uplift Indigenous data sovereignties.

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Authors and Affiliations

  1. Hugging Face, Brooklyn, New York, NY, USA

    Nima Boscarino

  2. The Biodesign Institute, Arizona State University, Tempe, AZ, USA

    Reed A. Cartwright

  3. School of Life Sciences, Arizona State University, Tempe, AZ, USA

    Reed A. Cartwright & Krystal S. Tsosie

  4. Department of Anthropology and Global Health, University of California, San Diego, La Jolla, CA, USA

    Keolu Fox

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  1. Nima Boscarino
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  4. Krystal S. Tsosie
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Correspondence to Nima Boscarino.

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The authors declare no competing interests.

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Nature Machine Intelligence thanks Jantina de Vries and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Boscarino, N., Cartwright, R.A., Fox, K. et al. Federated learning and Indigenous genomic data sovereignty. Nat Mach Intell 4, 909–911 (2022). https://doi.org/10.1038/s42256-022-00551-y

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