Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Comment
  • Published:

We need Afrocentric datasets for inclusive AI growth

Nature Human Behaviour volume 9, pages 842–845 (2025)Cite this article

Subjects

The growth of artificial intelligence (AI) in Africa faces a key challenge: a lack of quality data. This Comment explores datasets in agriculture, health and energy, and advocates for Afrocentric data that reflect African experiences.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

References

  1. Gondwe, G. Online Media Glob. Commun. 2, 228–249 (2023).

    Article  Google Scholar 

  2. Robinson, N., Ogayo, P., Mortensen, D. R. & Neubig, G. ChatGPT MT: competitive for high- (but not low-) resource languages. In Proc. 8th Conf. Machine Translation (eds Koehn, P. et al.) 392–418 (ACL, 2023).

  3. Okolo, C. T. The promise and perils of generative AI: case studies in an African context. In AfriCHI ’23: Proc. 4th African Human Computer Interaction Conf. (eds Jere, N. et al.) 266–270 (ACM, 2023).

  4. Asante, M. K. An Afrocentric Manifesto (John Wiley & Sons, 2013).

  5. Sendra-Balcells, C. et al. Sci. Rep. 13, 2728 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Lesmann, H. et al. Preprint at medRxiv https://doi.org/10.1101/2023.06.06.23290887 (2024).

  7. Adewole, M. et al. Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations (BraTS-Africa) (version 1) [Dataset]. The Cancer Imaging Archive https://doi.org/10.7937/v8h6-8x67 (2024).

  8. Aworinde, H. O. et al. Data Brief 50, 109517 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Barsagade, A. G., Ajalkar, D. A. & Rumale, A. S. Commun. Appl. Nonlinear Anal. 31, 470–484 (2024).

    Article  Google Scholar 

  10. Jolivot, A. et al. Earth Syst. Sci. Data 13, 5951–5967 (2021).

    Article  Google Scholar 

  11. Huang, X., Vrieling, A., Dou, Y., Belgiu, M. & Nelson, A. ISPRS J. Photogramm. Remote Sens. 218, 1–18 (2024).

    Article  Google Scholar 

  12. Sendra-Balcells, C. et al. Maternal fetal ultrasound planes from low-resource imaging settings in five African countries. Zenodo https://doi.org/10.5281/zenodo.7540448 (2023).

  13. Mensah, P. K. et al. Data Brief 49, 109306 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Jerry John Kponyo, Francis Kemausuor & Rachel Yayra Adjoe

  2. Responsible Artificial Intelligence Lab, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Eric Tutu Tchao & Henry Nunoo-Mensah

  3. Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Henry Nunoo-Mensah

Authors
  1. Jerry John Kponyo
    View author publications

    Search author on:PubMed Google Scholar

  2. Francis Kemausuor
    View author publications

    Search author on:PubMed Google Scholar

  3. Eric Tutu Tchao
    View author publications

    Search author on:PubMed Google Scholar

  4. Henry Nunoo-Mensah
    View author publications

    Search author on:PubMed Google Scholar

  5. Rachel Yayra Adjoe
    View author publications

    Search author on:PubMed Google Scholar

Corresponding author

Correspondence to Jerry John Kponyo.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kponyo, J.J., Kemausuor, F., Tchao, E.T. et al. We need Afrocentric datasets for inclusive AI growth. Nat Hum Behav 9, 842–845 (2025). https://doi.org/10.1038/s41562-025-02140-6

Download citation

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41562-025-02140-6

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics